# obfuscation

Published articles for obfuscation.

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## Illusion: Safeguarding Topology Confidentiality in LLM Multi-Agent Systems via Behavior-Preserving Obfuscation

DevFeed: [Illusion: Safeguarding Topology Confidentiality in LLM Multi-Agent Systems via Behavior-Preserving Obfuscation](<https://devfeed.tech/articles/illusion-safeguarding-topology-confidentiality-in-llm-multi-agent-systems-via-behavior-preserving-obfuscation-114036.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.00923>)

Author: Yiu-Fai Cheung, Hongzhi Guo, Miao Pan, Irwin King, Hao Wang

Published: 2026-11-03T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [LLM security](<https://devfeed.tech/topics/llm-security.md>), [Multi Agent Systems](<https://devfeed.tech/topics/multi-agent-systems.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [llm](<https://devfeed.tech/tags/llm.md>), [multi-agent-systems](<https://devfeed.tech/tags/multi-agent-systems.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [topology](<https://devfeed.tech/tags/topology.md>)

### AI overview

Illusion conceals LLM multi-agent communication topologies by adding carefully selected fake edges and messages that create a deceptive view for attackers while aiming to preserve output distributions and task accuracy. In reported experiments, it reduced edge-level F1 from above 0.87 to 0.62 and graph similarity from above 0.94 to 0.71. The abstract says the fake edges resisted similarity-based and LLM-judge filtering, including adaptive pruning, while largely preserving output distributions and task performance.

### Source excerpt

arXiv:2611.00923v1 Announce Type: new Abstract: The communication topology of Large Language Model Multi-Agent Systems (LLM-MASs) governs how information propagates among agents and encodes critical domain knowledge and expert workflow design. It is valuable intellectual property and plays a decisive role in system utility and safety, yet remains vulnerable to inference attacks that stealthily reconstruct it from agent outputs. Existing defenses detect or mitigate such attacks but do not proactively conceal the topology itself. We propose \textit{Illusion}, a proactive topology obfuscation defense for LLM-MAS. \textit{Illusion} injects fake edges and fake messages to present attackers with a deceptive topology, while selecting low-impact candidates that preserve output-distribution equivalence and task-accuracy non-inferiority. The resulting fake edges are designed to be indistinguishable from genuine messages under black-box observation. Extensive experiments show that \textit{Illusion} reduces edge-level F1 from over 0.87 to 0.62 and graph similarity from over 0.94 to 0.71. It also largely preserves output distributions and task performance, and its fake edges resist both similarity-based and LLM-judge filtering, even under adaptive pruning.

## Protecting Floating-Point Computation for DNN Binaries with MBA Obfuscation

DevFeed: [Protecting Floating-Point Computation for DNN Binaries with MBA Obfuscation](<https://devfeed.tech/articles/protecting-floating-point-computation-for-dnn-binaries-with-mba-obfuscation-108995.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2607.17603>)

Author: Yikun Hu, Zichen Zhao, Peixiang Qin, Ziyi Zhou, Jiaping Gui, Yuandao Cai, Wensheng Tang

Published: 2026-10-30T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [floating-point](<https://devfeed.tech/topics/floating-point.md>), [Security & Privacy](<https://devfeed.tech/topics/security-privacy.md>), [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [arithmetic](<https://devfeed.tech/tags/arithmetic.md>), [binaries](<https://devfeed.tech/tags/binaries.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [data-privacy](<https://devfeed.tech/tags/data-privacy.md>), [devices](<https://devfeed.tech/tags/devices.md>), [dnn](<https://devfeed.tech/tags/dnn.md>), [error](<https://devfeed.tech/tags/error.md>), [floating-point](<https://devfeed.tech/tags/floating-point.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [performance](<https://devfeed.tech/tags/performance.md>), [precision](<https://devfeed.tech/tags/precision.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [theft](<https://devfeed.tech/tags/theft.md>)

### AI overview

FLOB aims to protect compiled DNN models from reverse engineering by obfuscating their floating-point computations with Mixed Boolean-Arithmetic (MBA) transformations. It lifts values into a higher-precision binary expansion space, performs computation there, and projects results back to the target precision. The authors report semantic equivalence under the original format without added rounding error, alongside a protection-performance trade-off. Against existing reverse-engineering analyzers and deobfuscators, FLOB reduced operator recovery to 4.51% on average, a 32.82 percentage-point reduction versus existing methods.

### Source excerpt

arXiv:2607.17603v4 Announce Type: replace Abstract: Deep neural networks (DNNs) have become a foundational component of modern computing systems with a wide range of applications, such as computer vision, edge intelligence, etc. For the sake of low latency and data privacy, DNN models are increasingly compiled into executables and deployed on local devices. However, that exposes the models to model theft, enabling adversaries to recover proprietary assets via reverse engineering techniques. While code obfuscation naturally emerges for protecting executables from reverse engineering, existing schemes are primarily designed for traditional programs, focusing on complex control flow structures and integer-based operations. They are fundamentally inappropriate for DNN binaries, which exhibit relatively simple code structures and heavily rely on floating-point computation. In this paper, we propose FLOB, an obfuscation framework that protects floating-point computations in DNN binaries using Mixed Boolean-Arithmetic (MBA) transformations. Our approach lifts floating-point values into a higher-precision binary expansion space and performs MBA transformations at the representation level. The computation is carried out entirely in the lifted space, and the final result is then projected back to the target precision, yielding outputs that are semantically equivalent under the original floating-point format, without introducing additional rounding error. The experimental results show that FLOB outperforms the state-of-the-art obfuscation schemes against existing reverse engineering analyzers and deobfuscators, while preserving computational correctness without introducing additional numerical error, allowing flexible trade-off between protection and performance. Specifically, FLOB reduces the operator recovery rate to 4.51% on average, achieving a 32.82 percentage-point reduction compared to existing methods.

## Systematic Evaluation of Adversarial Jailbreaking Attacks and Defenses Across Seven Large Language Models

DevFeed: [Systematic Evaluation of Adversarial Jailbreaking Attacks and Defenses Across Seven Large Language Models](<https://devfeed.tech/articles/systematic-evaluation-of-adversarial-jailbreaking-attacks-and-defenses-across-seven-large-language-models-109074.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.39509>)

Author: Md. Robiul Islam Niloy, Abu Sayed Md. Mostafizur Rahaman

Published: 2026-10-30T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [LLM security](<https://devfeed.tech/topics/llm-security.md>), [Adversarial attacks](<https://devfeed.tech/topics/adversarial-attacks.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [adversarial](<https://devfeed.tech/tags/adversarial.md>), [attacks](<https://devfeed.tech/tags/attacks.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [defense](<https://devfeed.tech/tags/defense.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [robustness](<https://devfeed.tech/tags/robustness.md>), [safety](<https://devfeed.tech/tags/safety.md>)

### AI overview

Across seven LLMs, obfuscation was the most transferable jailbreak family, with a mean attack success rate of 54.3% and nonzero success against every model tested. Model size did not significantly predict robustness (Spearman r=-0.57, p=0.18), and models in the same architecture family had markedly different vulnerabilities. Of four defenses evaluated on the two most vulnerable models, self-reminder prompting was the most consistent: it reduced attack success from 44% to 10% on Mistral-7B and from 48% to 5% on Llama-3.1-8B, with zero false positives and no additional inference cost. The abstract does not report comparative results for the other defenses.

### Source excerpt

arXiv:2610.39509v1 Announce Type: new Abstract: Large language models (LLMs) deployed in safety-critical settings must resist adversarial inputs designed to elicit harmful outputs. We present a systematic empirical evaluation of five adversarial jailbreaking attack families---direct requests, roleplay-based jailbreaking, hypothetical framing, instruction override, and obfuscation---across seven open-weight and API-accessible LLMs: Llama-3-8B, Llama-3.1-8B, Llama-3.2-3B, Mistral-7B, Qwen2.5-7B, Gemma2-9B, and Phi-4-14B. We further evaluate four defense mechanisms: keyword blacklisting, self-reminder prompting, paraphrase-based sanitization, and perplexity-based filtering against the two most vulnerable models. Our results reveal that obfuscation attacks achieve a mean attack success rate (ASR) of 54.3% across all models and constitute the most transferable adversarial vector, bypassing every evaluated model with nonzero success. We find that model size does not predict adversarial robustness (Spearman r=-0.57, p=0.18), and that models within the same architecture family exhibit dramatically different vulnerability profiles. Self-reminder prompting achieves the most consistent defense, reducing ASR from 44% to 10% on Mistral-7B and from 48% to 5% on Llama-3.1-8B with zero false positives and no additional inference cost. Our code and datasets are publicly available at https://github.com/Mdniloykhan/jailbreak-eval-7llms.

## BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition

DevFeed: [BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition](<https://devfeed.tech/articles/bishield-tee-on-the-in-security-of-unilateral-weight-obfuscation-in-on-device-tee-shielded-llm-partition-109007.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.21565>)

Author: Zhiyuan Wang, Yuezhi Zhou, Ziqi Zhang, Zhu Zhang, Zhixing Tan, Yongheng Deng

Published: 2026-10-30T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [LLM security](<https://devfeed.tech/topics/llm-security.md>), [Confidential Computing](<https://devfeed.tech/topics/confidential-computing.md>), [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>)

Tags: [attack](<https://devfeed.tech/tags/attack.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [inference-efficiency](<https://devfeed.tech/tags/inference-efficiency.md>), [intellectual-property](<https://devfeed.tech/tags/intellectual-property.md>), [llm](<https://devfeed.tech/tags/llm.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [privacy](<https://devfeed.tech/tags/privacy.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [security](<https://devfeed.tech/tags/security.md>), [tee](<https://devfeed.tech/tags/tee.md>)

### AI overview

The abstract reports that unilateral output-side weight obfuscation in TEE-shielded on-device LLMs has a Solution Determinacy Property: the secret can be uniquely determined, allowing attackers to expose the true key structure. Attacks on GroupCover and ArrowCloak reportedly reached over 2.4 times the full-shielding black-box baseline and recovered near-complete model functionality. BiShield-TEE instead jointly obfuscates input and output dimensions to leave many equally valid key candidates. Across the tested models and datasets, its attack accuracy stayed at the full-shielding black-box baseline, and it achieved higher inference efficiency than prior methods on real hardware.

### Source excerpt

arXiv:2610.21565v3 Announce Type: replace Abstract: On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse engineering and intellectual property theft. Trusted Execution Environments (TEEs) provide hardware-enforced isolation for on-device inference, yet most TEE-based parameter obfuscation relies on unilateral matrix transformations that obfuscate only the output side. We identify a vulnerability in this design, termed the Solution Determinacy Property (SDP): the obfuscation secret is uniquely determined, exposing the structure of the true key and enabling attackers to break the protection. Under our attacks, representative methods GroupCover and ArrowCloak are reduced to negligible protection, with attack accuracy reaching over 2.4 times the full-shielding black-box baseline and recovering near-complete model functionality. To address this vulnerability, we propose BiShield-TEE, a bilateral framework that induces Ambiguous Solutions through joint obfuscation of input and output dimensions, hiding the true key among many equally valid candidates. Experiments across diverse models and datasets show that BiShield-TEE confines attack accuracy to the full-shielding black-box baseline, while achieving higher inference efficiency than prior methods on real hardware. Our code is available at https://github.com/laduiw/BiShield-TEE.

## SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs

DevFeed: [SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs](<https://devfeed.tech/articles/semdiff-binary-similarity-detection-by-diffing-key-semantics-graphs-103309.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2308.01463>)

Author: Zian Liu, Xiaoxi Meng, Viet Vo, Siqi Ma, Chao Chen, Dongxi Liu, Jun Zhang, Shigang Liu, Muhammad Ejaz Ahmed, Yang Xiang

Published: 2026-10-28T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [graph theory](<https://devfeed.tech/topics/graph-theory.md>), [Compilers and Linkers](<https://devfeed.tech/topics/compilers-and-linkers.md>)

Tags: [binaries](<https://devfeed.tech/tags/binaries.md>), [bug](<https://devfeed.tech/tags/bug.md>), [compilers](<https://devfeed.tech/tags/compilers.md>), [control](<https://devfeed.tech/tags/control.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [detection](<https://devfeed.tech/tags/detection.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [malware](<https://devfeed.tech/tags/malware.md>), [malware-analysis](<https://devfeed.tech/tags/malware-analysis.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [optimizations](<https://devfeed.tech/tags/optimizations.md>), [plagiarism](<https://devfeed.tech/tags/plagiarism.md>), [prototype](<https://devfeed.tech/tags/prototype.md>), [search](<https://devfeed.tech/tags/search.md>), [semantics](<https://devfeed.tech/tags/semantics.md>), [similarity](<https://devfeed.tech/tags/similarity.md>), [sort](<https://devfeed.tech/tags/sort.md>), [version](<https://devfeed.tech/tags/version.md>), [vulnerabilities](<https://devfeed.tech/tags/vulnerabilities.md>)

### AI overview

SemDiff aims to detect similar binaries despite changes in optimization, compiler, source version, or obfuscation by comparing key code behaviors rather than syntax. It extracts key instructions, converts them to key expressions, and builds a graph following binary control flow. For comparison, it topologically serializes each graph, tokenizes and concatenates the expressions, then uses locality-sensitive hashes to estimate similarity. The abstract reports that SemDiff outperformed state-of-the-art tools on binaries built with different optimization levels, compilers, and obfuscations, and was effective for library version search and finding similar firmware vulnerabilities.

### Source excerpt

arXiv:2308.01463v2 Announce Type: replace Abstract: Binary similarity detection is a critical technique that has been applied in many real-world scenarios where source code is not available, e.g., bug search, malware analysis, and code plagiarism detection. Existing works are ineffective in detecting similar binaries in cases where different compiling optimizations, compilers, source code versions, or obfuscation are deployed. We observe that all the cases do not change a binary's key code behaviors although they significantly modify its syntax and structure. With this key observation, we extract a set of key instructions from a binary to capture its key code behaviors. By detecting the similarity between two binaries' key instructions, we can address well the ineffectiveness limitation of existing works. Specifically, we translate each extracted key instruction into a self-defined key expression, generating a key-semantics graph based on the binary's control flow. Each node in the key-semantics graph denotes a key instruction, and the node attribute is the key expression. To quantify the similarity between two given key-semantics graphs, we first serialize each graph into a sequence of key expressions by topological sort. Then, we tokenize and concatenate key expressions to generate token lists. We calculate the locality-sensitive hash value for all token lists and quantify their similarity. %We implement a prototype, called SemDiff, consisting of two modules: graph generation and graph diffing. The first module generates a pair of key-semantics graphs and the second module diffs the graphs. Our evaluation results show that overall, SemDiff outperforms state-of-the-art tools when detecting the similarity of binaries generated from different optimization levels, compilers, and obfuscations. SemDiff is also effective for library version search and finding similar vulnerabilities in firmware.

## CVE-2026-116588 - Datadog dd-trace-py: ReDOS in URL query string obfuscation

DevFeed: [CVE-2026-116588 - Datadog dd-trace-py: ReDOS in URL query string obfuscation](<https://devfeed.tech/articles/cve-2026-116588-datadog-dd-trace-py-redos-in-url-query-string-obfuscation-100815.md>)

Original publisher: [Read original article](<https://cvefeed.io/vuln/detail/CVE-2026-116588>)

Author: CVEFEED.io; github.com

Published: 2026-10-26T22:16:55Z

Content type: news

Language: en

Sources: [Latest High/Critical Vulnerabilitiy Feed](<https://devfeed.tech/sources/latest-high-critical-vulnerabilitiy-feed.md>)

Topics: [Regular expression](<https://devfeed.tech/topics/regular-expression.md>), [Query (disambiguation)](<https://devfeed.tech/topics/query.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [datadog](<https://devfeed.tech/topics/datadog.md>)

Tags: [1-5](<https://devfeed.tech/tags/1-5.md>), [2026](<https://devfeed.tech/tags/2026.md>), [3](<https://devfeed.tech/tags/3.md>), [apm](<https://devfeed.tech/tags/apm.md>), [cause](<https://devfeed.tech/tags/cause.md>), [client](<https://devfeed.tech/tags/client.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cve](<https://devfeed.tech/tags/cve.md>), [cve-2026-116588](<https://devfeed.tech/tags/cve-2026-116588.md>), [cvss](<https://devfeed.tech/tags/cvss.md>), [datadog](<https://devfeed.tech/tags/datadog.md>), [expression](<https://devfeed.tech/tags/expression.md>), [fixed](<https://devfeed.tech/tags/fixed.md>), [http](<https://devfeed.tech/tags/http.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [published](<https://devfeed.tech/tags/published.md>), [query](<https://devfeed.tech/tags/query.md>), [string](<https://devfeed.tech/tags/string.md>), [trace](<https://devfeed.tech/tags/trace.md>), [url](<https://devfeed.tech/tags/url.md>)

### AI overview

Datadog dd-trace-py versions from 1.5.0 up to (but not including) 3.19.9 and 4.15.6 are vulnerable to excessive backtracking in the default query-string obfuscation regex. In affected HTTP tracing integrations that include query strings in URL span tags, the regex processes attacker-controlled input synchronously and is enabled by default; a remote attacker can cause prolonged CPU use, request latency and service unavailability. The issue is fixed in versions 3.19.9 and 4.15.6.

### Source excerpt

CVE ID :CVE-2026-116588 Published : Oct. 26, 2026, 10:16 p.m. | 32 minutes ago Description :Datadog dd-trace-py is the Datadog Python APM client. From 1.5.0 until 3.19.9 and 4.15.6, the default query-string obfuscation regular expression can exhibit excessive backtracking while synchronously processing attacker-controlled URL query strings for HTTP span metadata. A remote attacker can cause prolonged CPU consumption, request latency, and service unavailability in affected HTTP tracing integrations that include query strings in URL span tags. The vulnerable processing is enabled by default for those integrations. This issue is fixed in versions 3.19.9 and 4.15.6. Severity: 8.7 | HIGH Visit the link for more details, such as CVSS details, affected products, timeline, and more...

## AgentTrace: Execution-Level Fingerprinting of Black-Box LLM Agents

DevFeed: [AgentTrace: Execution-Level Fingerprinting of Black-Box LLM Agents](<https://devfeed.tech/articles/agenttrace-execution-level-fingerprinting-of-black-box-llm-agents-99942.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.32092>)

Author: Gejian Zhao, Hanzhou Wu, Xinpeng Zhang

Published: 2026-10-26T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [LLM security](<https://devfeed.tech/topics/llm-security.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [adversarial](<https://devfeed.tech/tags/adversarial.md>), [agents](<https://devfeed.tech/tags/agents.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [behaviors](<https://devfeed.tech/tags/behaviors.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [execution](<https://devfeed.tech/tags/execution.md>), [family](<https://devfeed.tech/tags/family.md>), [foundation](<https://devfeed.tech/tags/foundation.md>), [foundation-model](<https://devfeed.tech/tags/foundation-model.md>), [framework](<https://devfeed.tech/tags/framework.md>), [generation](<https://devfeed.tech/tags/generation.md>), [identity](<https://devfeed.tech/tags/identity.md>), [interfaces](<https://devfeed.tech/tags/interfaces.md>), [lightweight](<https://devfeed.tech/tags/lightweight.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [markov](<https://devfeed.tech/tags/markov.md>), [model](<https://devfeed.tech/tags/model.md>), [modeling](<https://devfeed.tech/tags/modeling.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>)

### AI overview

AgentTrace identifies the foundation model behind a black-box LLM agent by injecting structurally ambiguous probes at rigid tool interfaces and analyzing the resulting execution traces. Initial serialization failures separate model families; the sequence of repair operations provides finer-grained signals, including for evaluated Llama-3 8B/70B variants that share tokenizers. Lightweight Markov chains model these repair trajectories. The abstract reports 96.0% attribution accuracy under adversarial identity spoofing and says the method outperforms unordered behavioral statistics while remaining robust to prompt obfuscation and temperature scaling.

### Source excerpt

arXiv:2610.32092v1 Announce Type: cross Abstract: Verifying the provenance of autonomous LLM agents is a critical security challenge. Existing semantic attribution is easily obfuscated by system prompts, while active watermarking conflicts with schema-constrained tool generation. We propose AgentTrace, an active framework that identifies the foundation model behind a black-box agent via observable execution traces. AgentTrace injects structurally ambiguous probes to trigger tokenization-sensitive serialization failures at rigid tool interfaces. While these initial failures provide coarse-grained separation across model families, the temporal sequence of subsequent repair operations reveals fine-grained, model-specific recovery behaviors. By modeling these multi-step trajectories with lightweight Markov chains, AgentTrace achieves 96.0% attribution accuracy under adversarial identity spoofing. Crucially, sequential repair modeling successfully distinguishes the evaluated intra-family variants sharing identical tokenizers (e.g., Llama-3 8B/70B), substantially outperforming unordered behavioral statistics while remaining robust against prompt obfuscation and temperature scaling.

## Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement

DevFeed: [Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement](<https://devfeed.tech/articles/taming-cot-obfuscation-in-vlms-from-mechanistic-evidence-to-activation-level-enforcement-99835.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2609.24243>)

Author: Xutao Mao, Jianing Zhu, Jinman Zhao, Tongliang Liu, Xiaowen Chu, Cong Wang, Bo Han

Published: 2026-10-26T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>)

Topics: [llm-reasoning](<https://devfeed.tech/topics/llm-reasoning.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [grpo](<https://devfeed.tech/topics/grpo.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [activation](<https://devfeed.tech/tags/activation.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [autoencoders](<https://devfeed.tech/tags/autoencoders.md>), [based](<https://devfeed.tech/tags/based.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [can](<https://devfeed.tech/tags/can.md>), [capabilities](<https://devfeed.tech/tags/capabilities.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [code](<https://devfeed.tech/tags/code.md>), [constraint](<https://devfeed.tech/tags/constraint.md>), [content](<https://devfeed.tech/tags/content.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [experiments](<https://devfeed.tech/tags/experiments.md>), [features](<https://devfeed.tech/tags/features.md>), [feedback](<https://devfeed.tech/tags/feedback.md>), [framework](<https://devfeed.tech/tags/framework.md>), [general](<https://devfeed.tech/tags/general.md>), [grounding](<https://devfeed.tech/tags/grounding.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

The abstract reports that RL-induced chain-of-thought obfuscation in vision-language models coincides with task-agnostic template features becoming less separable from visually grounded content in activation space. TAME targets this shift during RL training: an anti-obfuscation feedback loop reduces behavioral obfuscation, while an asymmetric Sparse Autoencoder constraint penalizes template activations and preserves visual evidence. Across two model families, it improves CoT monitorability over GRPO by up to 30.9 points on VIRL-39k and 16.7 points on SPA-VL, while largely preserving accuracy and general capabilities across four standard VLM benchmarks; the abstract notes task-specific trade-offs.

### Source excerpt

arXiv:2609.24243v3 Announce Type: replace Abstract: Reinforcement learning (RL) has substantially improved the reasoning capabilities of vision-language models (VLMs), yet it can induce chain-of-thought (CoT) obfuscation, an operational, non-intentional regime where models achieve high accuracy while producing reasoning traces that are less grounded and harder to monitor. While prior work documents this decay at the behavioral level, the underlying representational correlates of such drift remain poorly understood. In this work, we find that obfuscation is accompanied by a representational shift, where RL-induced optimization makes task-agnostic template features less separable from visually grounded content in the model's activation space. Based on this insight, we propose TAME, an activation-level intervention framework that leverages Sparse Autoencoders (SAEs) to regularize VLM internal features during RL training. Specifically, TAME utilizes an anti-obfuscation feedback loop to reduce behavioral obfuscation and applies an asymmetric SAE constraint to specific internal directions, penalizing template activations while preserving visual evidence. By directly intervening on the features associated with obfuscation, our method improves the transparency and visual grounding of reasoning traces. Experiments on the VIRL-39k and SPA-VL benchmarks across two model families show that TAME improves CoT monitorability (by up to +30.9 points on VIRL and +16.7 points on SPA-VL over GRPO), while largely preserving task accuracy and general-capability performance across four standard VLM benchmarks, with task-specific trade-offs. Our code is available at https://github.com/henrymao2004/TAME.

## VMSpec: A Decompiler-based Approach against Virtual Machine Obfuscation

DevFeed: [VMSpec: A Decompiler-based Approach against Virtual Machine Obfuscation](<https://devfeed.tech/articles/vmspec-a-decompiler-based-approach-against-virtual-machine-obfuscation-94537.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.29566>)

Author: Fabian Kilger, Leonard Auer, Manuel Andreas

Published: 2026-10-22T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>), [code analysis](<https://devfeed.tech/topics/code-analysis.md>), [Ghidra](<https://devfeed.tech/topics/ghidra.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [approach](<https://devfeed.tech/tags/approach.md>), [behavior](<https://devfeed.tech/tags/behavior.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [bytecode](<https://devfeed.tech/tags/bytecode.md>), [code](<https://devfeed.tech/tags/code.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [decompiler](<https://devfeed.tech/tags/decompiler.md>), [generation](<https://devfeed.tech/tags/generation.md>), [ghidra](<https://devfeed.tech/tags/ghidra.md>), [hash](<https://devfeed.tech/tags/hash.md>), [infinite](<https://devfeed.tech/tags/infinite.md>), [input](<https://devfeed.tech/tags/input.md>), [interpreter](<https://devfeed.tech/tags/interpreter.md>), [malware](<https://devfeed.tech/tags/malware.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [semantics](<https://devfeed.tech/tags/semantics.md>), [virtual-machine](<https://devfeed.tech/tags/virtual-machine.md>)

### AI overview

VMSpec addresses a gap in VM deobfuscation by automatically constructing processor specifications so established decompilers can recover higher-level program semantics from VM-obfuscated code. The authors evaluate a prototype on 147 benchmark programs obfuscated with Tigress and Code Virtualizer, plus six domain generation algorithms from real-world malware. They report that it removes VM obfuscation from nearly all samples, with output comparable to Ghidra's decompilation of the original code, and can reconstruct complete behavior for programs with input-dependent paths and recursive calls. These are claims from the supplied abstract; it does not provide detailed methods, per-configuration results, or limitations.

### Source excerpt

arXiv:2610.29566v1 Announce Type: new Abstract: Malicious actors frequently obfuscate their code to hinder investigative reverse engineering efforts. Among the most formidable techniques is virtual machine (VM) obfuscation: programs are translated into bytecode for a custom VM, often with a randomized virtual instruction set and distributed alongside a corresponding interpreter. Manual analysis and deobfuscation scale poorly due to the complexity of VM obfuscation and the diversification techniques that render each VM and interpreter unique. Existing methods primarily focus on simplifying execution traces and identifying virtual execution boundaries. However, comparatively little attention has been given to recovering higher-level program semantics from VM-obfuscated code. To address this gap, we propose VMSpec, a novel decompiler-based approach that automates the construction of VM processor specifications, enabling existing, well-known decompilers to analyze the VM-obfuscated code. We evaluate our prototype of VMSpec on 147 programs from an established obfuscation benchmark, comprising hash functions, standard algorithms, and randomized functions, obfuscated with different configurations of the Tigress and Code Virtualizer obfuscators. To demonstrate real-world applicability, we additionally include six domain generation algorithms stemming from real-world malware. VMSpec completely strips VM-obfuscation from nearly all obfuscated samples, producing output comparable to Ghidra's decompilation of the original code. Furthermore, it is the first method capable of reconstructing the complete program behavior of VM-obfuscated programs with infinite, input-dependent paths and recursive calls.

## Mind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-node Attack

DevFeed: [Mind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-node Attack](<https://devfeed.tech/articles/mind-your-boundaries-boundary-aware-model-obfuscation-against-cross-node-attack-93789.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.27135>)

Author: Yuyang Zhang, Yihui Jin, Kangjie Chen, Leyang Wu, Run Wang

Published: 2026-10-21T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [Security](<https://devfeed.tech/topics/security.md>), [Adversarial attacks](<https://devfeed.tech/topics/adversarial-attacks.md>)

Tags: [adversarial](<https://devfeed.tech/tags/adversarial.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-chips](<https://devfeed.tech/tags/ai-chips.md>), [attack](<https://devfeed.tech/tags/attack.md>), [cross](<https://devfeed.tech/tags/cross.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [defense](<https://devfeed.tech/tags/defense.md>), [device](<https://devfeed.tech/tags/device.md>), [devices](<https://devfeed.tech/tags/devices.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [edge](<https://devfeed.tech/tags/edge.md>), [learning](<https://devfeed.tech/tags/learning.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [machine](<https://devfeed.tech/tags/machine.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [model-robustness](<https://devfeed.tech/tags/model-robustness.md>), [models](<https://devfeed.tech/tags/models.md>), [node](<https://devfeed.tech/tags/node.md>), [noise](<https://devfeed.tech/tags/noise.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>)

### AI overview

BMO aims to limit cross-node attacks by making models distributed from the same source less interchangeable after an attacker obtains white-box access to one device. It decomposes model parameters into boundary-position and boundary-curvature components using null-space constraints, then injects noise into the curvature component to diversify decision boundaries while preserving benign accuracy. Across four adversarial attack types and four on-device models, the abstract reports a 68.68% average defense success rate, compared with 22.32% for the strongest baseline, and comparable benign accuracy. These are abstract-reported results; the supplied text does not provide further evaluation details.

### Source excerpt

arXiv:2610.27135v1 Announce Type: new Abstract: With the advancement of powerful AI chips, on-device machine learning is gaining traction for its low latency and privacy protection. However, when edge devices are compromised, attackers can obtain white-box access to the deployed models by reversing the apps directly to obtain the knowledge of peer devices, which is known as Cross-Node Attacks, like performing efficient adversarial attacks. Most existing defenses target known vulnerabilities, yet white-box access still enables attackers to uncover more unknown weaknesses. This creates a critical risk for edge-device deployments, where one compromised device can endanger all models distributed from the same source. Motivated by findings that the model robustness is primarily influenced by the curvature of the decision boundary, we investigate how model parameters shape its curvature. In this paper, we propose a novel method, BMO, that obfuscates model parameters to diversify the decision boundary further resisting cross-node attack while preserving its benign accuracy. Specifically, we explore decomposing model parameters into boundary-position and boundary-curvature components based on null-space constraints, then models with differentiated decision boundaries are instantiated by injecting noise into the boundary-curvature component. Across all four major types of adversarial attacks with four widely-deployed on-device models, BMO achieves an average success rate of 68.68% in defense, far exceeding the most competitive baseline, which attains only 22.32% in defending against cross-node attacks. Moreover, BMO achieves comparable performance in benign accuracy compared with baselines.

## Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices

DevFeed: [Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices](<https://devfeed.tech/articles/model-exposure-on-one-device-endangers-others-rethinking-edge-neural-network-security-across-devices-93790.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.27152>)

Author: Yuyang Zhang, Kangjie Chen, Yihui Jin, Run Wang

Published: 2026-10-21T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Cryptography and Security](<https://devfeed.tech/sources/arxiv-cryptography-and-security.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>)

Tags: [adversarial](<https://devfeed.tech/tags/adversarial.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [attack](<https://devfeed.tech/tags/attack.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cr](<https://devfeed.tech/tags/cs-cr.md>), [defense](<https://devfeed.tech/tags/defense.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [diversity](<https://devfeed.tech/tags/diversity.md>), [edge](<https://devfeed.tech/tags/edge.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [iot](<https://devfeed.tech/tags/iot.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [model](<https://devfeed.tech/tags/model.md>), [network](<https://devfeed.tech/tags/network.md>), [network-security](<https://devfeed.tech/tags/network-security.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [privacy](<https://devfeed.tech/tags/privacy.md>), [unpacked](<https://devfeed.tech/tags/unpacked.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vision-transformers](<https://devfeed.tech/tags/vision-transformers.md>)

### AI overview

When centrally distributed edge devices share the same neural network, an attacker who extracts a model from one device can craft adversarial examples that transfer to other devices. VeilNN aims to reduce this cross-node transfer while preserving task functionality by introducing model diversity through architectural and parameter obfuscation, including network cell obfuscation and layer assembling. The abstract says it applies to MLPs, CNNs, and Vision Transformers. Across four attack types, experiments report 49.19% average defense success, up to 81.19%, and more than 33.16% improvement over existing defenses.

### Source excerpt

arXiv:2610.27152v1 Announce Type: new Abstract: Neural networks (NNs) are increasingly deployed on edge devices such as mobile and IoT platforms, offering low latency and improved privacy compared to cloud-based deployment. However, on-device models embedded in mobile applications can be unpacked and exposed to adversaries, enabling cross-node attacks in centrally distributed ecosystems, where adversarial examples crafted on one compromised device transfer effectively to other nodes sharing the same models. To address this threat, we propose VeilNN, a defense framework that reduces adversarial transferability across on-device models via model obfuscation. The core idea of VeilNN is to introduce controlled model diversity while preserving original task functionality. Specifically, VeilNN applies stealthy and irreversible obfuscation at both the architectural and parameter levels through network cell obfuscation and layer assembling, and is applicable to diverse NN architectures, including MLPs, CNNs, and Vision Transformers. Extensive experiments demonstrate that VeilNN effectively defends against cross-node attacks, achieving an average defense success of 49.19% and up to 81.19% across four adversarial attack types, with more than 33.16% improvement over existing defenses.

## RemoteThreat Launches With $7 Million for Offensive Operations Platform

DevFeed: [RemoteThreat Launches With $7 Million for Offensive Operations Platform](<https://devfeed.tech/articles/remotethreat-launches-with-7-million-for-offensive-operations-platform-61751.md>)

Original publisher: [Read original article](<https://www.securityweek.com/remotethreat-launches-with-7-million-for-offensive-operations-platform/>)

Author: SecurityWeek News

Published: 2026-09-29T14:38:07Z

Content type: news

Language: en

Sources: [SecurityWeek](<https://devfeed.tech/sources/securityweek.md>)

Topics: [Agentic SOC](<https://devfeed.tech/topics/agentic-soc.md>), [anthropic](<https://devfeed.tech/topics/anthropic.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai-assistants](<https://devfeed.tech/tags/ai-assistants.md>), [c2](<https://devfeed.tech/tags/c2.md>), [command](<https://devfeed.tech/tags/command.md>), [critical-infrastructure](<https://devfeed.tech/tags/critical-infrastructure.md>), [cyber](<https://devfeed.tech/tags/cyber.md>), [cybersecurity-funding](<https://devfeed.tech/tags/cybersecurity-funding.md>), [datatribe](<https://devfeed.tech/tags/datatribe.md>), [exploitation](<https://devfeed.tech/tags/exploitation.md>), [funding](<https://devfeed.tech/tags/funding.md>), [general](<https://devfeed.tech/tags/general.md>), [hacker](<https://devfeed.tech/tags/hacker.md>), [initial-access](<https://devfeed.tech/tags/initial-access.md>), [o-c-o-platform](<https://devfeed.tech/tags/o-c-o-platform.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [offensive-cyber-operations](<https://devfeed.tech/tags/offensive-cyber-operations.md>), [offensive-security](<https://devfeed.tech/tags/offensive-security.md>), [operator](<https://devfeed.tech/tags/operator.md>), [osage-university-partners](<https://devfeed.tech/tags/osage-university-partners.md>), [pre-seed](<https://devfeed.tech/tags/pre-seed.md>), [pre-seed-funding](<https://devfeed.tech/tags/pre-seed-funding.md>), [remotethreat](<https://devfeed.tech/tags/remotethreat.md>)

### AI overview

RemoteThreat launched from stealth with $7 million in pre-seed funding to develop an offensive cyber operations platform. Its O/C/O Platform combines mission planning, command and control, implants, access and obfuscation tools, targeting and analysis systems, and AI assistants for enterprise and critical infrastructure red teams, government mission teams, and vetted defense partners. Operations can be manual, AI-assisted, or delegated to autonomous workflows, with policies, approvals, and traceable decisions governing consequential actions.

### Source excerpt

The company emerged from stealth mode with pre-seed funding from Osage University Partners and DataTribe. The post RemoteThreat Launches With $7 Million for Offensive Operations Platform appeared first on SecurityWeek.

## Attacker compromised nearly 1000 Zyxel switches since August (CVE-2026-7273)

DevFeed: [Attacker compromised nearly 1000 Zyxel switches since August (CVE-2026-7273)](<https://devfeed.tech/articles/attacker-compromised-nearly-1000-zyxel-switches-since-august-cve-2026-7273-57915.md>)

Original publisher: [Read original article](<https://www.helpnetsecurity.com/2026/09/22/zyxel-switches-cve-2026-7273-vulnerability-exploited/>)

Author: Zeljka Zorz

Published: 2026-09-22T10:42:54Z

Content type: news

Language: en

Sources: [Help Net Security](<https://devfeed.tech/sources/help-net-security.md>)

Topics: [Exploit](<https://devfeed.tech/topics/exploit.md>), [Security](<https://devfeed.tech/topics/security.md>), [10 Gigabit Ethernet](<https://devfeed.tech/topics/10gbe-networking.md>), [sensitive data](<https://devfeed.tech/topics/sensitive-data.md>), [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [Python](<https://devfeed.tech/topics/python.md>), [Appwrite](<https://devfeed.tech/topics/appwrite.md>), [WordPress](<https://devfeed.tech/topics/wordpress.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [arctic-wolf-networks](<https://devfeed.tech/tags/arctic-wolf-networks.md>), [cve](<https://devfeed.tech/tags/cve.md>), [devices](<https://devfeed.tech/tags/devices.md>), [don-t-miss](<https://devfeed.tech/tags/don-t-miss.md>), [exploit](<https://devfeed.tech/tags/exploit.md>), [exploitation](<https://devfeed.tech/tags/exploitation.md>), [exploited](<https://devfeed.tech/tags/exploited.md>), [firmware](<https://devfeed.tech/tags/firmware.md>), [gigabit-ethernet](<https://devfeed.tech/tags/gigabit-ethernet.md>), [gitea](<https://devfeed.tech/tags/gitea.md>), [government](<https://devfeed.tech/tags/government.md>), [greynoise](<https://devfeed.tech/tags/greynoise.md>), [gs1900](<https://devfeed.tech/tags/gs1900.md>), [hot-stuff](<https://devfeed.tech/tags/hot-stuff.md>), [http](<https://devfeed.tech/tags/http.md>), [networking](<https://devfeed.tech/tags/networking.md>), [news](<https://devfeed.tech/tags/news.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [python](<https://devfeed.tech/tags/python.md>), [security](<https://devfeed.tech/tags/security.md>), [sensitive-data](<https://devfeed.tech/tags/sensitive-data.md>), [smbs](<https://devfeed.tech/tags/smbs.md>), [veeam-software](<https://devfeed.tech/tags/veeam-software.md>), [vulnerability](<https://devfeed.tech/tags/vulnerability.md>), [zyxel](<https://devfeed.tech/tags/zyxel.md>)

### AI overview

A threat actor exploited CVE-2026-7273 in unpatched Zyxel GS1900 switches, compromising 996 devices across 48 countries and exfiltrating configuration, networking information, and hashed root credentials. The vulnerability enables unauthenticated OS command execution through a crafted HTTP request and was patched in June 2026.

### Source excerpt

A Chinese-speaking threat actor has exploited a vulnerability (CVE-2026-7273) in unpatched ZyXEL GS1900 Smart Managed Switches and has exfiltrated sensitive data from 996 devices across 48 countries, GreyNoise reported on Monday. The affected switches are predominantly located in Italy, the US, Taiwan, South Korea, and a number of EU countries. CVE-2026-7273 exploitation is part of an unfolding operation The Zyxel GS1900 Series is a line of Gigabit Ethernet switches aimed at small and mid-sized business ... More -> The post Attacker compromised nearly 1000 Zyxel switches since August (CVE-2026-7273) appeared first on Help Net Security.

## Obfuscation Fundamentals: Applying ProGuard and R8 Protections to Android Code

DevFeed: [Obfuscation Fundamentals: Applying ProGuard and R8 Protections to Android Code](<https://devfeed.tech/articles/obfuscation-32342.md>)

Original publisher: [Read original article](<https://dustn.dev/page/presentations/2019-07-24-obfuscation-fundamentals/>)

Author: dustin@dustn.dev (Dustin Summers)

Published: 2026-09-17T04:13:15.404035Z

Content type: tutorial

Language: en

Sources: [Dustin Summers](<https://devfeed.tech/sources/dustin-summers.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [Android](<https://devfeed.tech/topics/android.md>), [R8](<https://devfeed.tech/topics/r8.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [meetup](<https://devfeed.tech/tags/meetup.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [proguard](<https://devfeed.tech/tags/proguard.md>), [r8](<https://devfeed.tech/tags/r8.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

A presentation introducing obfuscation with ProGuard and R8, focused on applying protections to Android code. It explains how to diagnose the code entering and leaving these build tools.

### Source excerpt

This presentation was given at a MeetUp in DC. It corresponds to this series of blog posts, which is the first in a series to teach about Obfuscation/ProGuard/R8, and will primarily be centered around applying protections to Android Code. Tools such as R8 and ProGuard are available, but to many they are a black box that code goes into and stuff comes out. The following will demystify the black box, and teach developers how to properly diagnose the code going into and coming out of these build tools.

## The only perfect Endpoint Prevention and Response (EPR) score in 2026 belongs to Elastic

DevFeed: [The only perfect Endpoint Prevention and Response (EPR) score in 2026 belongs to Elastic](<https://devfeed.tech/articles/the-only-perfect-endpoint-prevention-and-response-epr-score-in-2026-belongs-to-elastic-26916.md>)

Original publisher: [Read original article](<https://www.elastic.co/blog/av-comparatives-epr-test-2026>)

Author: Mia LaVada

Published: 2026-09-15T00:00:00Z

Content type: article

Language: en

Sources: [Elastic Blog](<https://devfeed.tech/sources/elastic-blog-elasticsearch-kibana-and-elk-stack.md>)

Topics: [Endpoint Security & XDR](<https://devfeed.tech/topics/endpoint-security-xdr.md>), [Software Testing](<https://devfeed.tech/topics/software-testing.md>), [Security](<https://devfeed.tech/topics/security.md>), [Security Operations Center](<https://devfeed.tech/topics/security-operations-center.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [alert-fatigue](<https://devfeed.tech/tags/alert-fatigue.md>), [analysts](<https://devfeed.tech/tags/analysts.md>), [investigation-incident-response-security-compliance-security-analytics-xdr](<https://devfeed.tech/tags/investigation-incident-response-security-compliance-security-analytics-xdr.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [persistence](<https://devfeed.tech/tags/persistence.md>), [protection](<https://devfeed.tech/tags/protection.md>), [security](<https://devfeed.tech/tags/security.md>), [security-endpoint-security](<https://devfeed.tech/tags/security-endpoint-security.md>), [soc](<https://devfeed.tech/tags/soc.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [testing](<https://devfeed.tech/tags/testing.md>), [tooling](<https://devfeed.tech/tags/tooling.md>), [usb](<https://devfeed.tech/tags/usb.md>)

### AI overview

Elastic describes its results in the 2026 AV-Comparatives Endpoint Prevention and Response test, reporting 100% protection scores, zero false alerts, and the lowest modeled operational footprint among tested products. The article explains that Elastic stopped all 50 attack scenarios at the initial phase.

### Source excerpt

Elastic sits at the very top of this year's AV-Comparatives' CyberRisk Quadrant within the 2026 Endpoint Prevention and Response (EPR) test with the highest protection scores at 100%. Learn more.

## The extension you never installed: KREMLIN forges Chrome's own integrity checks to steal banking sessions

DevFeed: [The extension you never installed: KREMLIN forges Chrome's own integrity checks to steal banking sessions](<https://devfeed.tech/articles/the-extension-you-never-installed-kremlin-forges-chrome-s-own-integrity-checks-to-steal-banking-sessions-49078.md>)

Original publisher: [Read original article](<https://www.elastic.co/security-labs/threat-command/malicious-browser-extension-kremlin-banking-malware>)

Author: Cyril François,Andrew Pease

Published: 2026-09-14T00:00:00Z

Content type: article

Language: en

Sources: [Elastic Security Labs](<https://devfeed.tech/sources/elastic-security-labs.md>)

Topics: [Security](<https://devfeed.tech/topics/security.md>), [Docro Hijacker](<https://devfeed.tech/topics/docro-hijacker.md>), [WebExtension](<https://devfeed.tech/topics/webextension.md>), [Google Chrome](<https://devfeed.tech/topics/google-chrome.md>), [C2](<https://devfeed.tech/topics/c2.md>), [Edge](<https://devfeed.tech/topics/edge.md>), [Ethereum](<https://devfeed.tech/topics/ethereum.md>), [JavaScript](<https://devfeed.tech/topics/javascript.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>)

Tags: [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [c2](<https://devfeed.tech/tags/c2.md>), [chrome](<https://devfeed.tech/tags/chrome.md>), [edge](<https://devfeed.tech/tags/edge.md>), [ethereum](<https://devfeed.tech/tags/ethereum.md>), [extensions](<https://devfeed.tech/tags/extensions.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [malware](<https://devfeed.tech/tags/malware.md>), [malware-analysis](<https://devfeed.tech/tags/malware-analysis.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [payload](<https://devfeed.tech/tags/payload.md>), [security](<https://devfeed.tech/tags/security.md>), [sensitive-data](<https://devfeed.tech/tags/sensitive-data.md>), [threat-intelligence](<https://devfeed.tech/tags/threat-intelligence.md>)

### AI overview

Elastic Security Labs analyzes KREMLIN, a malicious browser-extension malware operation targeting Brazilian banking users across seven campaigns over 15 months. The report details its JavaScript loaders, C++ installers, Chrome and Edge persistence techniques, and use of Ethereum smart contracts to manage C2 configuration.

### Source excerpt

Elastic Security Labs tracked this malicious browser extension across seven campaigns and 15 months, through Brazilian bank lures and the Ethereum smart contracts that hold its C2 configuration.

## JavaScript obfuscation: From party trick to phishing kit

DevFeed: [JavaScript obfuscation: From party trick to phishing kit](<https://devfeed.tech/articles/javascript-obfuscation-from-party-trick-to-phishing-kit-49137.md>)

Original publisher: [Read original article](<https://blog.talosintelligence.com/javascript-obfuscation-from-party-trick-to-phishing-kit/>)

Author: James Hodgkinson

Published: 2026-08-27T10:00:27Z

Content type: tutorial

Language: en

Sources: [Cisco Talos Blog](<https://devfeed.tech/sources/cisco-talos-blog.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [JavaScript](<https://devfeed.tech/topics/javascript.md>)

Tags: [encryption](<https://devfeed.tech/tags/encryption.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [malware](<https://devfeed.tech/tags/malware.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [phishing](<https://devfeed.tech/tags/phishing.md>), [tool-talk](<https://devfeed.tech/tags/tool-talk.md>)

### AI overview

A practical introduction to JavaScript obfuscation, explaining why analysts reverse it and outlining approaches for examining suspicious scripts from phishing kits, malware, and compromised sites. It also emphasizes isolating hostile samples and protecting sensitive credentials during analysis.

### Source excerpt

Learn the basics of what obfuscation is, why a researcher would try to reverse it, and several ways to approach the problem.

## Defeating AI-Assisted Reverse Engineering (or at Least Trying To)

DevFeed: [Defeating AI-Assisted Reverse Engineering (or at Least Trying To)](<https://devfeed.tech/articles/defeating-ai-assisted-reverse-engineering-or-at-least-trying-to-92088.md>)

Original publisher: [Read original article](<http://blog.quarkslab.com/defeating-ai-assisted-reverse-engineering-or-at-least-trying-to.html>)

Author: Rémy Salim

Published: 2026-08-19T22:00:00Z

Content type: article

Language: en

Sources: [Quarkslab](<https://devfeed.tech/sources/quarkslab.md>)

Topics: [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>), [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [code analysis](<https://devfeed.tech/topics/code-analysis.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-assisted](<https://devfeed.tech/tags/ai-assisted.md>), [ai-assisted-reverse-engineering](<https://devfeed.tech/tags/ai-assisted-reverse-engineering.md>), [binary-analysis](<https://devfeed.tech/tags/binary-analysis.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-assisted](<https://devfeed.tech/tags/llm-assisted.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [rasp](<https://devfeed.tech/tags/rasp.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [strings](<https://devfeed.tech/tags/strings.md>)

### AI overview

In the tested setup, AI agents did not defeat the binary protections; obfuscation pushed them toward execution and cheaper ways to obtain plausible answers. Experiments exposed major evaluation failures: an agent found a plaintext answer key, runtime checks and suggestive filenames steered analyses into unsupported narratives, and reports or scripts sometimes claimed emulation that had not happened. The authors argue that a correct-looking result does not verify how it was obtained. Their proposed defense layers runtime-only secret recovery with varied, obscured environment checks, mixes sensor values into key material and obfuscation state, and returns plausible wrong outputs instead of identifiable crashes. They qualify the result as an exploratory first round: obfuscation still raises attacker cost, while agents increase analysis throughput.

### Source excerpt

Is LLM-assisted reverse engineering making obfuscation pointless? We spent a couple of weeks trying to find out, by handing sandboxed agents a series of progressively hardened AArch64 binaries and one prompt: recover the hidden strings inside. This post walks through what the agent actually did, three ways our experiment fell apart, and what those failures suggest about designing protections that hold against automated analysis.

## Tinder cuts app cold starts by 47% with new R8 Configuration Analyzer

DevFeed: [Tinder cuts app cold starts by 47% with new R8 Configuration Analyzer](<https://devfeed.tech/articles/tinder-cuts-app-cold-starts-by-47-with-new-r8-configuration-analyzer-4241.md>)

Original publisher: [Read original article](<https://android-developers.googleblog.com/2026/08/tinder-app-cold-start-r8-configuration-analyzer.html>)

Author: Android Developers (noreply@blogger.com)

Published: 2026-08-18T18:00:00Z

Content type: article

Language: en

Sources: [Android Developers](<https://devfeed.tech/sources/android-developers-blog.md>)

Topics: [R8](<https://devfeed.tech/topics/r8.md>), [Android](<https://devfeed.tech/topics/android.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [configuration](<https://devfeed.tech/topics/configuration.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [android](<https://devfeed.tech/tags/android.md>), [development](<https://devfeed.tech/tags/development.md>), [errors](<https://devfeed.tech/tags/errors.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [r8](<https://devfeed.tech/tags/r8.md>), [startup](<https://devfeed.tech/tags/startup.md>), [time](<https://devfeed.tech/tags/time.md>)

### AI overview

The article describes how Tinder used the R8 Configuration Analyzer to identify and remove keep-rule blockers in its Android application. The effort reduced cold-start time by 47%, app download size by 28.98% to 61.5 MB, and user-perceived ANRs by 28%.

### Source excerpt

Posted by Ajesh R Pai, Developer Relations Engineer, Ulises Uriel Verduzco Diaz, Software Engineer, Tinder, and Tracy Agyemang, Product Marketing Manager Tinder is on a mission to power and inspire real connections by making meeting easy and fun for every new generation of singles. However, as their Android application codebase grew in size, so did its complexity. Prior to their latest optimization efforts, approximately 70% of the application was not optimized, carrying 17 dex files,including three dedicated just to startup. Although they had enabled R8, much of its optimization potential was blocked due to keep rules, and the team was unable to identify which specific rules were preventing optimization. To reduce startup time and decrease user-perceived Application Not Responding (ANR) errors, Tinder turned to the new R8 Configuration Analyzer to tackle these challenges. By utilizing the R8 Configuration Analyzer, Tinder successfully identified and removed unintentional optimization blockers. The results were immediate and impactful: Tinder achieved a 47% reduction in app cold starts, shrank their app download size by 28.98% (down to 61.5 MB), and reduced user-perceived ANRs by 28%. Configuration analyzer The R8 Configuration Analyzer shows R8 optimization by tracking shrinking, optimization, and obfuscation scores to show available refinement areas. It shows the broad, redundant, or obsolete keep rules, including those from external libraries so that you can analyse the keep rule impact and refine the keep rules. Key metrics shown in Configuration Analyzer include: Shrinking Score: Code percentage available for R8 shrinking. Optimization Score: Code percentage open to optimization (for example, method inlining, horizontal class merging). Obfuscation Score: Percentage of classes, methods and fields that can be renamed by R8 to decrease size. Use the analyzer to audit keep rules and their impacts: Find broad rules: Narrow the scope of package-wide rules that restri

## App Hardening: One Obfuscation Pipeline Across Every Port

DevFeed: [App Hardening: One Obfuscation Pipeline Across Every Port](<https://devfeed.tech/articles/app-hardening-one-obfuscation-pipeline-across-every-port-19196.md>)

Original publisher: [Read original article](<https://www.codenameone.com/blog/app-hardening-cross-platform/>)

Author: Shai Almog

Published: 2026-08-15T00:00:00Z

Content type: article

Language: en

Sources: [CodeName One](<https://devfeed.tech/sources/codename-one.md>)

Topics: [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [cross-platform](<https://devfeed.tech/topics/cross-platform.md>), [Encryption](<https://devfeed.tech/topics/encryption.md>), [Security](<https://devfeed.tech/topics/security.md>), [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>), [App](<https://devfeed.tech/topics/app.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [app](<https://devfeed.tech/tags/app.md>), [cross-platform](<https://devfeed.tech/tags/cross-platform.md>), [encryption](<https://devfeed.tech/tags/encryption.md>), [hardening](<https://devfeed.tech/tags/hardening.md>), [ios](<https://devfeed.tech/tags/ios.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [native](<https://devfeed.tech/tags/native.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [platform](<https://devfeed.tech/tags/platform.md>), [r8](<https://devfeed.tech/tags/r8.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

Codename One App Hardening applies one pre-build protection pipeline to a merged cross-platform application before it is split into Android, iOS, JavaScript, Windows, Linux, and desktop builds. It supports renaming, eligible string encryption, selected control-flow transformations, verification, and crash-report retracing, while acknowledging that reverse engineering cannot be made impossible.

### Source excerpt

Codename One App Hardening applies class and member renaming, string encryption, and platform-safe control-flow transforms before one application becomes Android, iOS, JavaScript, or native desktop output.

## Token Jacking: Cybercriminals Could Be Stealing Your AI Resources

DevFeed: [Token Jacking: Cybercriminals Could Be Stealing Your AI Resources](<https://devfeed.tech/articles/token-jacking-cybercriminals-could-be-stealing-your-ai-resources-7746.md>)

Original publisher: [Read original article](<https://unit42.paloaltonetworks.com/ai-token-jacking/>)

Author: Unit 42

Published: 2026-08-06T10:00:49Z

Content type: article

Language: en

Sources: [Unit 42](<https://devfeed.tech/sources/unit-42.md>)

Topics: [token jacking](<https://devfeed.tech/topics/token-jacking.md>), [ai security](<https://devfeed.tech/topics/ai-security.md>), [API keys](<https://devfeed.tech/topics/api-keys.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Authentication](<https://devfeed.tech/topics/authentication.md>), [transfer stations](<https://devfeed.tech/topics/transfer-stations.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-api](<https://devfeed.tech/tags/ai-api.md>), [ai-gateway](<https://devfeed.tech/tags/ai-gateway.md>), [ai-security](<https://devfeed.tech/tags/ai-security.md>), [api-keys](<https://devfeed.tech/tags/api-keys.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [malware](<https://devfeed.tech/tags/malware.md>), [npm-packages](<https://devfeed.tech/tags/npm-packages.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [security](<https://devfeed.tech/tags/security.md>), [threat-research](<https://devfeed.tech/tags/threat-research.md>), [token-jacking](<https://devfeed.tech/tags/token-jacking.md>), [transfer-stations](<https://devfeed.tech/tags/transfer-stations.md>)

### AI overview

The article explains how criminals steal developers' AI API keys and use the resulting tokens to consume costly language-model resources, causing rapid financial losses. It outlines the role of authentication, automated access keys, token-based billing, and weak billing controls, and recommends security hygiene and AI protection measures.

### Source excerpt

Discover how attackers hijack AI tokens to fuel gray market transfer stations by stealing developer API keys. The post Token Jacking: Cybercriminals Could Be Stealing Your AI Resources appeared first on Unit 42.

## Mobile App Security Without Sacrificing UX | Guardsquare

DevFeed: [Mobile App Security Without Sacrificing UX | Guardsquare](<https://devfeed.tech/articles/mobile-app-security-without-sacrificing-ux-guardsquare-26308.md>)

Original publisher: [Read original article](<https://www.guardsquare.com/blog/mobile-app-profiling-security-ux>)

Author: Ryan Lloyd - Chief Product Officer

Published: 2026-07-21T13:02:06Z

Content type: article

Language: en

Sources: [Guardsquare](<https://devfeed.tech/sources/guardsquare-blog.md>)

Topics: [Mobile Security](<https://devfeed.tech/topics/mobile-security.md>), [obfuscation](<https://devfeed.tech/topics/obfuscation.md>), [User Experience](<https://devfeed.tech/topics/user-experience.md>), [mobile-app-performance](<https://devfeed.tech/topics/mobile-app-performance.md>), [debugging](<https://devfeed.tech/topics/debugging.md>)

Tags: [app](<https://devfeed.tech/tags/app.md>), [application](<https://devfeed.tech/tags/application.md>), [application-security](<https://devfeed.tech/tags/application-security.md>), [applications](<https://devfeed.tech/tags/applications.md>), [certificate](<https://devfeed.tech/tags/certificate.md>), [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [debugger](<https://devfeed.tech/tags/debugger.md>), [detection](<https://devfeed.tech/tags/detection.md>), [intellectual-property](<https://devfeed.tech/tags/intellectual-property.md>), [jailbreak](<https://devfeed.tech/tags/jailbreak.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [mobile-app](<https://devfeed.tech/tags/mobile-app.md>), [mobile-app-security](<https://devfeed.tech/tags/mobile-app-security.md>), [mobile-application-security](<https://devfeed.tech/tags/mobile-application-security.md>), [modern](<https://devfeed.tech/tags/modern.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [other](<https://devfeed.tech/tags/other.md>), [security](<https://devfeed.tech/tags/security.md>), [technical](<https://devfeed.tech/tags/technical.md>), [ux](<https://devfeed.tech/tags/ux.md>)

### AI overview

Guardsquare's approach uses profiling data from an instrumented app build to guide where and how strongly it applies code obfuscation and runtime security checks. The article excerpt argues that profiling can help balance protection with app stability, performance, and user experience: obfuscation may break code or SDK behavior that relies on reflection, while obfuscation and runtime checks can add performance overhead, especially in apps that are not well optimized. It says AI-driven and agentic testing may make profiling easier to automate at scale. The excerpt ends mid-sentence, so it does not provide the full discussion or further practical steps.

### Source excerpt

Mobile application security has evolved significantly over the past decade. Modern applications routinely employ code obfuscation, runtime application self-protection (RASP), anti-tampering controls, jailbreak and root detection, debugger detection, certificate pinning, and a variety of other runtime defenses designed to protect intellectual property and sensitive user data.

## M-Red-Team: AsyncAPI Supply Chain Compromise via GitHub Actions

DevFeed: [M-Red-Team: AsyncAPI Supply Chain Compromise via GitHub Actions](<https://devfeed.tech/articles/m-red-team-asyncapi-supply-chain-compromise-via-github-actions-53894.md>)

Original publisher: [Read original article](<https://www.wiz.io/blog/m-red-team-asyncapi-supply-chain-compromise-via-github-actions>)

Author: Merav Bar

Published: 2026-07-14T10:33:36Z

Content type: news

Language: en

Sources: [Wiz](<https://devfeed.tech/sources/wiz-blog-rss-feed.md>)

Topics: [AsyncAPI Specification](<https://devfeed.tech/topics/asyncapi.md>), [GitHub Actions](<https://devfeed.tech/topics/github-actions.md>), [npm packages](<https://devfeed.tech/topics/npm-packages.md>), [Vulnerabilities](<https://devfeed.tech/topics/vulnerabilities.md>), [Exploit](<https://devfeed.tech/topics/exploit.md>), [Malware](<https://devfeed.tech/topics/malware.md>)

Tags: [command-and-control](<https://devfeed.tech/tags/command-and-control.md>), [github](<https://devfeed.tech/tags/github.md>), [github-actions](<https://devfeed.tech/tags/github-actions.md>), [npm](<https://devfeed.tech/tags/npm.md>), [npm-packages](<https://devfeed.tech/tags/npm-packages.md>), [npm-supply-chain-attack](<https://devfeed.tech/tags/npm-supply-chain-attack.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [payload](<https://devfeed.tech/tags/payload.md>), [persistence](<https://devfeed.tech/tags/persistence.md>), [personal-access-token](<https://devfeed.tech/tags/personal-access-token.md>), [pull-request](<https://devfeed.tech/tags/pull-request.md>), [pull-requests](<https://devfeed.tech/tags/pull-requests.md>), [repository](<https://devfeed.tech/tags/repository.md>), [research](<https://devfeed.tech/tags/research.md>), [supply-chain](<https://devfeed.tech/tags/supply-chain.md>), [supply-chain-attack](<https://devfeed.tech/tags/supply-chain-attack.md>), [threat-intel](<https://devfeed.tech/tags/threat-intel.md>), [vulnerability](<https://devfeed.tech/tags/vulnerability.md>)

### AI overview

An attacker exploited a misconfigured GitHub Actions workflow in the AsyncAPI generator repository to steal a privileged personal access token. The token was used to publish malicious npm package versions containing payloads that execute on import, establish persistence, and connect to command-and-control infrastructure.

### Source excerpt

Detect and mitigate malicious @asyncapi npm packages linked to the latest npm supply chain attack.

## How to Protect HTML Content from Being Copied

DevFeed: [How to Protect HTML Content from Being Copied](<https://devfeed.tech/articles/how-to-protect-html-content-from-being-copied-87922.md>)

Original publisher: [Read original article](<https://jsdev.space/howto/protect-from-copying/>)

Author: anton liberant

Published: 2026-07-14T00:00:00Z

Content type: tutorial

Language: en

Sources: [JavaScript Development](<https://devfeed.tech/sources/javascript-development.md>)

Topics: [Web Development](<https://devfeed.tech/topics/web-development.md>), [Web Content Accessibility Guidelines](<https://devfeed.tech/topics/web-content-accessibility-guidelines.md>), [Firefox extension](<https://devfeed.tech/topics/firefox-extension.md>)

Tags: [accessibility](<https://devfeed.tech/tags/accessibility.md>), [browser-extensions](<https://devfeed.tech/tags/browser-extensions.md>), [clipboard](<https://devfeed.tech/tags/clipboard.md>), [html](<https://devfeed.tech/tags/html.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [obfuscation](<https://devfeed.tech/tags/obfuscation.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

The tutorial compares ways to discourage copying website content, from CSS and JavaScript restrictions to clipboard attribution, Canvas rendering, and font obfuscation. It explains that all client-side methods can be bypassed and weighs their effects on usability, accessibility, and search indexing.

### Source excerpt

Learn what actually works to discourage copying content in HTML. Explore CSS, JavaScript, Canvas, clipboard APIs, font obfuscation.

[Next page](<https://devfeed.tech/tags/obfuscation.md?cursor=WyIyMDI2LTA3LTE0VDAwOjAwOjAwKzAwOjAwIiwgIjUyZTZjNjIyLWU3MGQtNDIwOS05NTYzLTZhODI3OWVlNTM4YiJd>)