# interest Published articles for interest. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why Your AI Application Is Exposed DevFeed: [Why Your AI Application Is Exposed]() Original publisher: [Read original article]() Author: Damian Tommasino Published: 2026-08-26T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [AI Development](), [Large Language Model](), [vulnerability](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)](), [Machine Learning, Security Attacks](), [data]() Tags: [ai](), [ai-red-team](), [application-security](), [awareness](), [blog](), [devops](), [executive](), [interest](), [llm](), [mcp](), [rag](), [security](), [testing](), [vulnerability]() ### AI overview The article explains why AI applications can pass conventional security scans and model evaluations while remaining exploitable through chained interactions among prompts, language models, retrieval, tools, and backend workflows. It presents DAST, AI penetration testing, and AI red teaming as complementary lenses for identifying exposed surfaces, exploitable paths, and real-world adversary outcomes. ### Source excerpt AI applications can pass security scans yet remain exploitable through chained attacks across models, tools, data, and business workflows. Learn how DAST, AI pentesting, and red teaming work together to expose end-to-end risk. ## Remediation Agents, Demystified: Why Fixing Beats Finding DevFeed: [Remediation Agents, Demystified: Why Fixing Beats Finding]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Vulnerabilities](), [AI-assisted coding](), [pull-requests](), [Responsibility & Safety](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [ai-agents](), [application-security](), [aspm](), [awareness](), [ci-cd](), [code-security](), [community](), [developer](), [devops](), [devsecops](), [enablement](), [interest](), [javascript](), [live-stream](), [node-js](), [pull-requests](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [validation](), [vs-code](), [vulnerabilities]() ### AI overview Snyk's Remediation Agent is presented as a public-preview solution for the growing security-issue backlog. The article explains how coding agents can increase vulnerabilities, why deterministic remediation advice has not been enough, and how validation helps turn fixes into mergeable pull requests. ### Source excerpt See how Snyk's Remediation Agent uses security intelligence, breakability analysis, and validation to turn vulnerabilities into mergeable pull requests. ## Benchmarking Secure-and-Functional Remediation and How Snyk Agent Fix Lifts Frontier-Model Fix Rates by over 14% DevFeed: [Benchmarking Secure-and-Functional Remediation and How Snyk Agent Fix Lifts Frontier-Model Fix Rates by over 14%]() Original publisher: [Read original article]() Author: Stephen Thoemmes Published: 2026-08-18T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [benchmarking](), [LLM evaluation / benchmarking](), [vulnerability](), [AI-assisted coding](), [JavaScript](), [Python]() Tags: [agentic](), [ai](), [awareness](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [code-security](), [developer](), [devops](), [evaluation](), [interest](), [java](), [javascript](), [performance](), [python](), [security](), [security-labs](), [snyk-code](), [snyk-security-intel](), [vulnerability](), [vulnerability-insights]() ### AI overview A benchmark of about 150 vulnerable JavaScript, Java, and Python samples evaluates whether frontier models produce fixes that are both secure and functional. The article reports that models working alone reach roughly 72-75%, while Snyk Intelligence raises Opus 4.6 from 74.6% to 85.4% and improves Python results from 64% to 88%. ### Source excerpt A benchmark of secure, functional vulnerability fixes across JavaScript, Java, and Python shows Snyk Intelligence helps frontier models break past a 72-75% performance plateau. ## Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS DevFeed: [Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS]() Original publisher: [Read original article]() Author: Nuno Loureiro; Luis Grangeia Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Vulnerabilities](), [Authorization](), [Software as a service](), [Microservice](), [Microservices](), [Front end](), [API](), [App](), [Artificial Intelligence](), [Trustworthy AI]() Tags: [agent](), [ai](), [api](), [application-security](), [applications](), [attacks](), [autonomous](), [awareness](), [blog](), [customer](), [customer-featured](), [cybersecurity](), [devops](), [enterprise](), [frontend](), [interest](), [microservices](), [saas](), [security](), [vulnerabilities](), [vulnerability-insights](), [web]() ### AI overview Evo Continuous Offensive Security (COS) is presented as an autonomous offensive-security system combining AI pentesting, agent red teaming, and dynamic testing. The article reports a real assessment of a multi-tenant enterprise SaaS application that uncovered 33 confirmed vulnerabilities, including tenant-wide compromise and critical authorization flaws. ### Source excerpt A real Evo Continuous Offensive Security assessment uncovered 33 confirmed vulnerabilities in a multi-tenant enterprise SaaS, including tenant-wide compromise and critical authorization flaws. ## A First Look at Evo Agentic AppSec: Agentic Remediation and Malicious Code Defense DevFeed: [A First Look at Evo Agentic AppSec: Agentic Remediation and Malicious Code Defense]() Original publisher: [Read original article]() Author: Brendan Hann Published: 2026-08-04T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI Bots]() Tags: [agent](), [agentic](), [ai](), [ai-agents](), [ai-security](), [application-security](), [aspm](), [autonomous](), [blog](), [cli](), [code-security](), [developer](), [devops](), [interest](), [javascript](), [node-js](), [open-source-security](), [pull-request](), [python](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [vulnerabilities](), [vulnerability-insights]() ### AI overview Snyk introduces Evo Agentic AppSec capabilities for automatically remediating vulnerabilities and blocking malicious packages before they reach code. The Remediation Agent triages issues, creates and checks fixes, and opens pull requests for review. ### Source excerpt Explore Snyk's first Agentic AppSec capabilities: an autonomous Remediation Agent that fixes vulnerabilities and Malicious Code Defense that blocks risky packages before they ship. ## Stop The Sprawl Snyk Secrets Now Generally Available DevFeed: [Stop The Sprawl Snyk Secrets Now Generally Available]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-08-04T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding](), [CI/CD]() Tags: [agentic](), [ai](), [ai-agents](), [ai-coding](), [ai-security](), [api-keys](), [application-security](), [blog](), [ci-cd](), [code-security](), [convert-paid](), [developer](), [devops](), [devsecops](), [interest](), [scm](), [secrets](), [security](), [snyk-platform]() ### AI overview Snyk announces general availability of Snyk Secrets, a contextual ML-powered secrets detection capability for its AI Security Platform. The article frames it as a response to faster secret exposure in AI-driven development and CI/CD workflows. ### Source excerpt Snyk Secrets is now generally available, bringing contextual ML detection, secure-at-commit prevention, and unified secrets governance to the Snyk AI Security Platform. ## Evo Continuous Offensive Security Is Here Pentesting Grade Coverage For The 350 Days A Year You Aren't Testing DevFeed: [Evo Continuous Offensive Security Is Here Pentesting Grade Coverage For The 350 Days A Year You Aren't Testing]() Original publisher: [Read original article]() Author: John Carione Published: 2026-08-04T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding]() Tags: [agentic](), [agents](), [ai](), [ai-coding](), [ai-security](), [application-security](), [aspm](), [attacks](), [autonomous](), [autonomous-agents](), [availability](), [black-hat](), [blog](), [cos](), [cybersecurity](), [developer](), [developers](), [development](), [devops](), [interest](), [security](), [snyk-platform](), [testing]() ### AI overview Snyk announces general availability of Evo Continuous Offensive Security, an autonomous, AI-powered pentesting offering intended to validate exploitable flaws between traditional pentests. The article frames the launch as part of an expanded AI security platform for AI-accelerated development. ### Source excerpt Snyk Evo Continuous Offensive Security brings autonomous, AI-powered pentesting to the 350 days between traditional tests, uncovering exploitable flaws attackers can find first. ## The Attacker Never Sleeps, Neither Can Your Testing DevFeed: [The Attacker Never Sleeps, Neither Can Your Testing]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-07-30T00:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agents](), [ai](), [application-security](), [awareness](), [blog](), [code](), [code-security](), [devops](), [devsecops](), [executive](), [finserv](), [interest](), [open-source-security](), [security](), [snyk-apprisk](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [software](), [supply-chain-security](), [tech](), [testing](), [vulnerability-insights]() ### AI overview AI-driven code generation is expanding software attack surfaces while giving attackers machine-speed reasoning capabilities. The article argues that security teams need continuous testing, agent governance, and independent validation of findings. ### Source excerpt AI is accelerating software development and giving attackers machine-speed capabilities. Security teams must continuously test AI-built code, govern agents, and independently validate every finding. ## Stadium Summer: The Snyk Connect Fan Zone Tour DevFeed: [Stadium Summer: The Snyk Connect Fan Zone Tour]() Original publisher: [Read original article]() Author: Samantha Lioanag; Alysa Turner Published: 2026-07-29T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding]() Tags: [ai](), [ai-security](), [blog](), [community](), [developer](), [engineering](), [event](), [interest](), [security](), [skills](), [vibe-coding](), [workshops]() ### AI overview Snyk's Fan Zone tour combined AI security workshops, networking, and hands-on challenges across eight cities and three virtual sessions. Activities included chatbot hacks, vibe-coding contests, and a browser-focused DAST threat-modeling exercise. ### Source excerpt Snyk's Fan Zone tour brought AI security workshops, networking, and friendly competition to 8 cities and 3 virtual sessions. Attendees built skills, shared ideas, and leveled up together. ## What Is AI Pentesting and How Does It Work? DevFeed: [What Is AI Pentesting and How Does It Work?]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-07-27T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Security](), [vulnerability](), [AI Models](), [Authorization](), [large-language-models](), [Reconnaissance](), [payload](), [SQL]() Tags: [ai](), [application-security](), [article](), [awareness](), [developer](), [devops](), [devsecops](), [interest](), [large-language-models](), [models](), [payload](), [security](), [security-labs](), [snyk-platform](), [sql](), [systems](), [tech](), [tools](), [vulnerabilities](), [vulnerability]() ### AI overview AI pentesting uses reasoning-capable models, deterministic testing tools, independent validation, and target context to continuously find, exploit, and verify application vulnerabilities that traditional scanners may miss. The article explains its workflow and contrasts context-dependent flaws with heuristic-detectable issues such as SQL injection and misconfigurations. ### Source excerpt AI pentesting uses reasoning-capable models to continuously find and validate the flaws scanners miss, especially broken authorization and business-logic abuse. ## Snyk VulnBench JS 1.0: Can LLMs Find the Same Bugs Twice? DevFeed: [Snyk VulnBench JS 1.0: Can LLMs Find the Same Bugs Twice?]() Original publisher: [Read original article]() Author: Liran Tal Published: 2026-06-29T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Vulnerabilities](), [AI-assisted coding]() Tags: [agentic](), [ai](), [application-security](), [awareness](), [benchmark](), [blog](), [claude](), [code-security](), [developer](), [devops](), [engineering](), [interest](), [llms](), [node-js](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [vulnerability](), [vulnerability-insights]() ### AI overview Snyk VulnBench JS 1.0 evaluates how consistently agentic LLM security reviews find vulnerabilities across identical repeated scans. Reference-matched findings were comparatively stable, while LLM-only reports varied substantially; the article argues for combining LLM review with deterministic SAST rather than replacing either approach. ### Source excerpt Snyk VulnBench JS 1.0: 300 repeated scans show LLM security findings vary by run, while SAST and models catch different vulnerability gaps. ## NVD in the AI Era: The Case for Multi-Source Vulnerability Intelligence DevFeed: [NVD in the AI Era: The Case for Multi-Source Vulnerability Intelligence]() Original publisher: [Read original article]() Author: Michelle Ginzburg; Noa Yaffe-Ermoza Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [open-source-security](), [Security](), [data]() Tags: [ai](), [article](), [awareness](), [developer](), [devops](), [interest](), [open-source](), [open-source-security](), [security](), [snyk-open-source](), [snyk-security-intel](), [software](), [supply-chain-security](), [technology](), [vulnerability](), [vulnerability-insights]() ### AI overview The article explains why vulnerability intelligence is moving beyond reliance on the National Vulnerability Database. It describes NIST's shift toward prioritized triage and presents Snyk's multi-source approach, combining vulnerability advisories, analyst validation, internal enrichment, open-source context, and multiple risk assessments to help teams prioritize remediation. ### Source excerpt NIST's shift to risk-based enrichment makes one thing clear: modern security teams need more than a single public source. In the AI era, trusted vulnerability intelligence depends on multiple signals, human validation, and clear context. ## The New Security Control Point: Governing AI Agents Inside the Execution Loop DevFeed: [The New Security Control Point: Governing AI Agents Inside the Execution Loop]() Original publisher: [Read original article]() Author: Agnieszka Koc Published: 2026-06-23T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Security](), [Application Security](), [AI Development](), [agent observability](), [real-time]() Tags: [agents](), [ai](), [ai-agents](), [api](), [application-security](), [autonomous](), [awareness](), [blog](), [code-security](), [developer](), [interest](), [process](), [security](), [software-development]() ### AI overview AI agents introduce security risk through the actions they take while using tools, accessing systems, invoking APIs, executing commands, and generating code. The article presents agent behavior governance inside the execution loop as a security control point for evaluating whether decisions are authorized, appropriate, and aligned with organizational policy. ### Source excerpt AI agents introduce security risk through the actions they take, not just the code they produce. Learn how agent behavior governance helps teams observe, steer, and block risky actions in real time. ## What nearly 10,000 developer environments reveal about agentic development risk DevFeed: [What nearly 10,000 developer environments reveal about agentic development risk]() Original publisher: [Read original article]() Author: Ricardo Miguel Silva Published: 2026-06-23T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI-assisted coding](), [supply-chain-security](), [Application Security](), [Model Context Protocol](), [Agent Skills](), [prompt injection](), [Security](), [software-development](), [Tooling]() Tags: [agentic](), [agents](), [ai](), [ai-coding](), [application-security](), [article](), [awareness](), [claude](), [code](), [cursor](), [developer](), [developers](), [development](), [devops](), [devsecops](), [enablement](), [interest](), [prompt-injection](), [secrets](), [security](), [security-labs](), [skills](), [snyk-code](), [snyk-platform](), [snyk-security-intel](), [software-development](), [supply-chain](), [supply-chain-security](), [systems](), [tool](), [tooling](), [tools](), [vs-code]() ### AI overview Snyk analyzed nearly 10,000 developer environments and found that agentic development is introducing a new software supply chain layer. The article reports widespread use of multiple AI coding environments, MCP servers, and agent skills, along with confirmed prompt injection findings and security exposure that AppSec teams need to govern. ### Source excerpt AI coding agents are adding a new layer to the software supply chain. Learn what Snyk found in nearly 10,000 developer environments and how to secure the tools, instructions, and permissions behind agentic development. ## Announcing Agentic Development Security (ADS) DevFeed: [Announcing Agentic Development Security (ADS)]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-06-23T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [Application Security](), [AI Agent](), [AI Development](), [Model Context Protocol (MCP)](), [MCP Server](), [ai-coding]() Tags: [agentic](), [ai](), [ai-agent](), [ai-coding](), [application-security](), [awareness](), [blog](), [developer](), [devops](), [enablement](), [executive](), [interest](), [mcp](), [mcp-server](), [securing-ai](), [security](), [snyk-platform](), [software-development](), [supply-chain-security](), [tech]() ### AI overview Snyk announces Agentic Development Security (ADS), an Evo solution for securing AI-driven software development. It embeds visibility, governance, and control into workflows where AI agents use tools, execute actions across systems, and generate production-ready code. ### Source excerpt Announcing Snyk Agentic Development Security, a new Evo solution that helps organizations securely adopt AI-driven development with visibility, governance, and control. ## The full Snyk AI Security Platform, free for open source maintainers DevFeed: [The full Snyk AI Security Platform, free for open source maintainers]() Original publisher: [Read original article]() Author: Brendan Hann Published: 2026-06-18T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Maintainers](), [Open Source](), [Security](), [vulnerability](), [open-source-security](), [Artificial Intelligence](), [Frontier Model](), [Remote Access Trojan](), [AI Infrastructure]() Tags: [agent](), [ai](), [ai-infrastructure](), [ai-security](), [article](), [awareness](), [bugs](), [developer](), [enablement](), [engineering](), [frontier-model](), [interest](), [maintainers](), [open-source](), [open-source-security](), [remote-access-trojan](), [security](), [snyk-code](), [snyk-container](), [snyk-iac](), [snyk-open-source](), [snyk-platform](), [snyk-team](), [software](), [supply-chain-security](), [vulnerability](), [vulnerability-insights]() ### AI overview Snyk's Secure Developer Program gives qualifying open source projects free access to the Snyk AI Security Platform. It is designed to help maintainers prioritize real vulnerability reports and produce validated, merge-ready fixes through the Snyk Remediation Agent. ### Source excerpt Open source maintainers are drowning in real vulnerability reports and need help prioritizing, fixing, and shipping remediation faster. Snyk's Secure Developer Program gives qualifying projects free access to the Snyk AI Security Platform. ## The Government Just Banned an AI Model. An Engineer's Perspective. DevFeed: [The Government Just Banned an AI Model. An Engineer's Perspective.]() Original publisher: [Read original article]() Author: Randall Degges Published: 2026-06-15T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Security](), [Vulnerabilities](), [anthropic](), [Jailbreak](), [Machine Learning, Security Attacks](), [Code review](), [migration]() Tags: [ai](), [analysis](), [anthropic](), [application-security](), [awareness](), [blog](), [bugs](), [code](), [compliance](), [developer](), [devops](), [executive](), [interest](), [jailbreak](), [migration](), [security](), [snyk-platform](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [tech](), [vulnerabilities](), [vulnerability-insights](), [work]() ### AI overview An engineer examines the abrupt government-ordered shutdown of Anthropic's Fable 5 and Mythos 5 AI models after a jailbreak exposed powerful vulnerability-finding capabilities. The article argues that dependence on an AI vendor can create a supply-chain risk for engineering and security workflows, emphasizing the need for contingency plans when model access can disappear without warning. ### Source excerpt A government order abruptly took down a powerful AI model, exposing a new kind of supply chain risk for engineering teams. Security leaders need contingency plans before the next model disappears. ## So You Have an AI Security Budget. Now what? DevFeed: [So You Have an AI Security Budget. Now what?]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-06-04T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [ai security](), [Securing AI](), [Security](), [coding](), [Model Context Protocol]() Tags: [agentic](), [ai](), [ai-agents](), [ai-security](), [application-security](), [aspm](), [awareness](), [blog](), [compliance](), [development](), [devops](), [devsecops](), [enablement](), [executive](), [interest](), [mcp](), [mcp-server](), [policy](), [security](), [snyk-apprisk](), [snyk-platform](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [tech]() ### AI overview The article argues that AI security budgets should prioritize unified visibility, governance, policy enforcement, risk assessment, adversarial testing, runtime protection, and audit evidence across the full AI lifecycle. It distinguishes between securing agents that build software and agents operating in production applications. ### Source excerpt An AI security budget should fund more than visibility. The real priority is unified governance and enforcement across agentic development and production apps. ## The New Security Risks of the Agentic Development Lifecycle DevFeed: [The New Security Risks of the Agentic Development Lifecycle]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-06-03T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Application Security](), [sdlc](), [Artificial Intelligence](), [Code]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [application-security](), [autonomous](), [awareness](), [blog](), [ci-cd](), [code](), [developer](), [development](), [devops](), [devsecops](), [enablement](), [interest](), [scm](), [sdlc](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [software](), [tech](), [workflows]() ### AI overview The article explains the agentic development lifecycle, in which AI agents plan, build, modify, test, and ship software by interacting with tools, codebases, data sources, and development environments. It argues that security must protect the process creating software as well as the resulting code, because agents can introduce risk through what they use, do, and generate. ### Source excerpt AI agents are changing how software gets built, and with it, where security risk begins. Learn why securing the process matters as much as securing the code. ## Protestware by open source maintainer to hinder agentic coding: The jqwik 1.10.0 Prompt Injection DevFeed: [Protestware by open source maintainer to hinder agentic coding: The jqwik 1.10.0 Prompt Injection]() Original publisher: [Read original article]() Author: Brian Vermeer Published: 2026-06-02T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [prompt injection](), [agentic-coding](), [AI-assisted coding](), [Open Source](), [Command-line interface](), [Maven](), [CI/CD](), [payload](), [Claude Code](), [cursor](), [GitHub Copilot]() Tags: [agentic-coding](), [agents](), [ai](), [ai-agents](), [ai-coding](), [article](), [awareness](), [ci-cd](), [claude-code](), [code](), [copilot](), [cursor](), [developer](), [devops](), [github](), [interest](), [java](), [open-source](), [open-source-security](), [payload](), [prompt-injection](), [sandbox](), [scm](), [security](), [security-labs](), [snyk-code](), [snyk-open-source](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [testing](), [tools](), [vulnerability-insights]() ### AI overview The article examines jqwik 1.10.0, whose maintainer embedded a prompt injection in test output aimed at AI coding agents. ANSI terminal escape codes concealed the instruction from humans while leaving it visible to tools processing raw logs, creating a supply-chain risk for CI/CD pipelines and agentic coding workflows. ### Source excerpt jqwik 1.10.0 added a hidden prompt injection aimed at AI coding agents, using terminal escape codes to conceal destructive instructions from humans while leaving them readable to logs and tools. ## Miasma supply chain attack: malicious code found in @redhat-cloud-services npm packages DevFeed: [Miasma supply chain attack: malicious code found in @redhat-cloud-services npm packages]() Original publisher: [Read original article]() Author: Brian Clark Published: 2026-06-01T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Malware](), [npm packages](), [redhat](), [incident](), [GitHub](), [OpenID connect (OIDC)](), [API](), [React](), [Cloud]() Tags: [api](), [article](), [awareness](), [ci-cd](), [cloud-security](), [developer](), [devops](), [devsecops](), [enablement](), [github](), [incident](), [interest](), [npm-packages](), [oidc](), [payload](), [react](), [redhat](), [sbom](), [scm](), [scope](), [secrets](), [security](), [snyk-open-source](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [tech](), [vulnerability-insights]() ### AI overview The Miasma supply chain attack compromised at least 32 @redhat-cloud-services npm package releases used by the Red Hat Hybrid Cloud Console. The malicious installation script steals developer and cloud credentials, attempts to spread through packages victims can publish, and may expose secrets on affected workstations and CI runners. ### Source excerpt A supply chain worm dubbed Miasma has been found in dozens of @redhat-cloud-services npm releases. The malicious preinstall hook steals credentials, probes cloud identities, and can republish other packages. ## Securing The AI Revolution: How Snyk And Our Partners Are Scaling For The Future DevFeed: [Securing The AI Revolution: How Snyk And Our Partners Are Scaling For The Future]() Original publisher: [Read original article]() Author: Tom Nielsen Published: 2026-05-21T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [ai security](), [AI-assisted coding](), [ai-coding](), [Claude Code](), [cursor](), [Anthropic Claude](), [atlassian](), [Amazon Web Services](), [Claude](), [OpenAI]() Tags: [agents](), [ai](), [ai-coding](), [ai-security](), [amazon](), [anthropic](), [article](), [atlassian](), [awareness](), [aws](), [claude](), [claude-code](), [code](), [cursor](), [customer](), [developers](), [devsecops](), [ecosystem](), [enablement](), [executive](), [interest](), [openai](), [partners](), [platform](), [related-content](), [scale](), [security](), [snyk-platform](), [snyk-security-intel](), [snyk-team](), [tech](), [workflows]() ### AI overview Snyk describes how AI coding agents are accelerating code creation beyond security teams' review capacity, pushing enterprises to govern AI-generated code at scale. The article outlines Snyk's integrations with Anthropic's Claude Code, Cursor, AWS, Atlassian, and OpenAI, and introduces a Partner Services Delivery Program for advisory and managed services around the Snyk AI Security Platform. ### Source excerpt AI is accelerating code creation. Learn how Snyk is scaling its AI Security Platform and investing in new partner programs to help enterprises govern AI-generated code at scale. ## A Day in the Life of a Strategy Co-Op in Snyk's Boston Office DevFeed: [A Day in the Life of a Strategy Co-Op in Snyk's Boston Office]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-05-20T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [vulnerability](), [Security]() Tags: [ai](), [ai-security](), [awareness](), [blog](), [career](), [collaboration](), [community](), [developer](), [devops](), [engineering](), [executive](), [interest](), [security](), [snyk-team](), [strategy](), [tech](), [vulnerability](), [work]() ### AI overview Lulu describes a typical day as a Strategy Co-Op at Snyk's Boston office, combining AI security work, cross-functional collaboration, mentorship, and a supportive workplace culture. ### Source excerpt Go behind the scenes with Lulu, a Strategy Co-Op at Snyk, and discover a day balancing high-impact AI security projects with a vibrant Boston office culture. ## "A Mini Shai-Hulud Has Appeared": Bun-Based Stealer Hits SAP @cap-js and mbt npm Packages DevFeed: ["A Mini Shai-Hulud Has Appeared": Bun-Based Stealer Hits SAP @cap-js and mbt npm Packages]() Original publisher: [Read original article]() Author: Stephen Thoemmes Published: 2026-04-29T00:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [incident](), [releases]() Tags: [article](), [awareness](), [ci-cd](), [developer](), [devops](), [ecosystem](), [github](), [github-actions](), [incident](), [interest](), [javascript](), [node-js](), [npm-packages](), [open-source-security](), [payload](), [releases](), [sap](), [scm](), [security](), [snyk-open-source](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [tech](), [tool](), [toolchain](), [vulnerability-insights]() ### AI overview A supply-chain attack compromised four npm packages in the SAP development ecosystem. Malicious releases download Bun and execute an obfuscated credential stealer, while SAP published clean post-incident versions and Snyk issued advisories. ### Source excerpt A new npm supply chain attack self-branded "Mini Shai-Hulud" compromised four SAP-ecosystem packages on April 29, 2026. Snyk has live advisories. Here's the technical breakdown, IOCs, and what to do. [Next page]()