# false positives Published articles for false positives. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## "Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE DevFeed: ["Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE]() Original publisher: [Read original article]() Author: Kent Marten; Sergei Fedorov Published: 2026-09-16T17:14:05Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [SQL](), [databricks](), [Cybersecurity](), [Security & Privacy]() Tags: [count](), [cybersecurity](), [databricks](), [failed](), [false-positives](), [functions](), [login](), [partition](), [product](), [real-time](), [regex](), [sql](), [window-functions]() ### AI overview This tutorial explains how Databricks supports SQL MATCH_RECOGNIZE for detecting ordered event patterns. It shows how the clause can simplify sequence detection, including identifying repeated login failures followed by a successful login, compared with complex SQL queries and window functions. ### Source excerpt Imagine you work in cybersecurity and you have a table that tracks login attempts... ## Abliterated Models Show Verdict Bias in FreeBSD Kernel Bug Hunting DevFeed: [Abliterated Models Show Verdict Bias in FreeBSD Kernel Bug Hunting]() Original publisher: [Read original article]() Author: clearbluejar Published: 2026-09-01T06:00:00Z Content type: article Language: en Sources: [clearbluejar]() Topics: [freebsd](), [vulnerability](), [bug](), [Kernel](), [gemma4](), [Testing](), [gemma]() Tags: [bug](), [bug-hunting](), [cve](), [false-positives](), [freebsd](), [gemma](), [gemma-4](), [kernel](), [llms](), [testing](), [vulnerability](), [vulnerability-research]() ### AI overview The article examines local abliterated open-weight models during vulnerability scanning of FreeBSD kernel source. Compared with base models of the same family and size, the modified models produced substantially more candidate and VALID findings, including false positives, while the most aggressive build did not surface the real CVE in a 28-file scan. The article describes this tendency as verdict bias: a greater willingness to return positive findings under uncertainty. ### Source excerpt Abliterated models never refuse, which makes them tempting for bug hunting. But on the same kernel source, they graduate three to four times as many findings to VALID, including false positives the base correctly rejects, and across a 28-file scan of FreeBSD's sys/rpc the most aggressive build never surfaced the real CVE at all. ## Your alt text passes automated checks. That doesn't mean it's any good. DevFeed: [Your alt text passes automated checks. That doesn't mean it's any good.]() Original publisher: [Read original article]() Author: Taarik Ashenafi Published: 2026-08-24T20:56:32Z Content type: article Language: en Sources: [GitHub]() Topics: [Accessibility](), [alt text](), [GitHub](), [Tooling]() Tags: [accessibility](), [alt-text](), [automation](), [blog](), [engineering](), [false-positives](), [github](), [github-actions](), [plugin](), [scanner](), [user-experience]() ### AI overview This article explains a GitHub Accessibility Scanner plugin for evaluating alt text. It distinguishes deterministic checks that automated tools can prove from contextual quality judgments that may require a model and image context. ### Source excerpt We built a plugin for the GitHub Accessibility Scanner to make sure your alt text is actually accessible. Here's how it works. The post Your alt text passes automated checks. That doesn't mean it's any good. appeared first on The GitHub Blog. ## 5 QE Pipeline Metrics That Show Where Quality Is Leaking DevFeed: [5 QE Pipeline Metrics That Show Where Quality Is Leaking]() Original publisher: [Read original article]() Author: Rick Crawford Published: 2026-08-21T15:00:10Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Development](), [Postman]() Tags: [api-testing](), [developer](), [development](), [false-positives](), [general](), [metrics](), [packages](), [policy](), [production](), [qe-program](), [quality-engineering](), [sdlc](), [shift-left](), [validation]() ### AI overview This developer article presents five quality-engineering pipeline metrics aligned with Design, Gate, Validate, Monitor, and Improve stages. The supplied text details package adoption, gate coverage and flake rate, and defect escape rate, explaining how they reveal uneven coverage, ineffective merge gates, false positives, and defects reaching production. ### Source excerpt In the last post I described six problems that compound when development outruns the QE infrastructure underneath it. Most leaders recognize all... The post 5 QE Pipeline Metrics That Show Where Quality Is Leaking appeared first on Postman Blog. ## Approval fatigue is agent governance's next attack surface DevFeed: [Approval fatigue is agent governance's next attack surface]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Security](), [Artificial Intelligence](), [Architecture & Design]() Tags: [agent](), [agentic](), [agentic-ai](), [alert-fatigue](), [false-positives](), [governance]() ### AI overview The article argues that repeated approval prompts can create approval fatigue in agentic AI systems. As requests accumulate, people may skim or approve them reflexively, weakening the intended safety control. It connects this pattern to alert fatigue and consent fatigue and discusses how attackers could intentionally trigger it through prompts. ### Source excerpt Security teams have studied alert fatigue for a decade. In 2026, attackers started writing prompts designed to trigger it in agents on purpose. ## DeepsecBench: evaluating model performance in finding cybersecurity vulnerabilities DevFeed: [DeepsecBench: evaluating model performance in finding cybersecurity vulnerabilities]() Original publisher: [Read original article]() Author: Eric Dodds Published: 2026-07-27T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Benchmark](), [Vulnerabilities](), [AI Models](), [Cybersecurity](), [Code](), [Open Source](), [OpenAI](), [hugging face]() Tags: [ai-models](), [analysis](), [benchmark](), [breach](), [code](), [cost](), [cybersecurity](), [false-positives](), [hugging-face](), [model](), [open-source](), [openai](), [testing](), [time](), [vulnerabilities]() ### AI overview DeepsecBench evaluates how well AI models find cybersecurity vulnerabilities in application code. It uses an open-source codebase, 50 entry-point files, and 231 human-judged findings, reporting recall, precision, cost, total time, and a recall-weighted F2 score. The article explains that the benchmark is kept secret to prevent models from training on its contents and describes how its results can guide security scanning programs. ### Source excerpt Last week, OpenAI evaluated two models on an exploit benchmark within an isolated sandbox. Guardrails were reduced for testing, and the models found a vulnerability in their environment, accessed the internet, and reached Hugging Face's production database. No human directed the action, but the breach is a clear example of how much more capable malicious attackers are when equipped with powerful AI models. But defenders have the same tools, and a clear advantage: knowledge of their own codebase. Hacks are initiated from the outside, so the single best defense is finding vulnerabilities from the inside before attackers do. Today we're releasing DeepsecBench, a benchmark that evaluates how well different models find cybersecurity vulnerabilities in application code. For each model the report includes recall, precision, cost, and total time, and combines recall and precision into a single benchmark score. Here is a sample of model performance from the leaderboard: Rank Model Level Score Cost Total time 1 GPT-5.6 Sol xhigh 35.58 $55.98 03:39:00 3 Claude Opus 5 medium 28.36 $31.96 00:47:01 8 Kimi K3 high 17.56 $12.38 01:59:00 10 Grok 4.5 high 15.58 $5.60 01:24:00 We built deepsec to make scanning as easy as possible. Now you can use the benchmark report to build a security scanning program that fits your budget and the complexity of your codebase, choosing the right mix of models to run and how often to run them. How the benchmark works DeepsecBench runs on an open-source codebase at a commit state just before a large number of vulnerabilities were fixed. We selected 50 entry-point files and built a golden set of 231 human-judged findings. Each model's score is a recall-weighted F2 (Score = 100 x 5PR/(4P+R)), weighting recall (R) twice as much as precision (P), because missed vulnerabilities will go unfixed, while false positives don't make your codebase less secure. Findings beyond the golden set are classified by a judge model as real or false, and count for or against ## Why the CVE doom cycle cannot be solved by working harder DevFeed: [Why the CVE doom cycle cannot be solved by working harder]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [vulnerability management](), [Automation](), [container-security](), [Vulnerabilities](), [configuration]() Tags: [automation](), [container-image-security](), [cve](), [false-positives](), [secure-by-design](), [security](), [vulnerability-management]() ### AI overview The article argues that the recurring cycle of scanning, triaging, patching, and redeploying container images cannot be solved by working harder or using faster scanners. Because vulnerability findings and CVEs accumulate faster than manual remediation can handle, it recommends embedding vulnerability management into the platform through secure-by-design practices, golden paths, and automation. ### Source excerpt Manual CVE triage doesn't scale. Break the CVE doom cycle by shifting vulnerability management into the platform with golden paths and automation ## Introducing new and improved reporting: How we are solving accessibility issue bloat and triage fatigue DevFeed: [Introducing new and improved reporting: How we are solving accessibility issue bloat and triage fatigue]() Original publisher: [Read original article]() Author: BrowserStack Team Published: 2026-06-10T04:51:54Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Accessibility](), [Testing](), [Website](), [Development]() Tags: [accessibility](), [component](), [deep-dive](), [false-positives](), [reports](), [testing](), [workflow]() ### AI overview BrowserStack introduces improved accessibility testing reports that group repeated or similar issues across pages, display confidence levels, and support guided bulk review. The changes aim to reduce issue bloat, help teams prioritize reliable violations, and cut triage time by up to 60%. ### Source excerpt Stop drowning in accessibility "issue bloat." BrowserStack's new reporting uses Intelligent Grouping and a Confidence Slider to turn thousands of redundant alerts into unique tasks. With Guided Bulk Review, you'll slash triage time by 60% and boost your score faster. ## DataAgents: How we turned 9 months of analysis into 10 days DevFeed: [DataAgents: How we turned 9 months of analysis into 10 days]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-06-09T22:42:18Z Content type: tutorial Language: en Sources: [Capital One Tech]() Topics: [Cloud](), [AI Agent](), [Amazon Web Services](), [Azure](), [Google Cloud Platform (GCP)](), [data]() Tags: [agentic-ai](), [ai](), [aws](), [azure](), [cloud](), [cloud-platform](), [data-analysis](), [false-positives](), [gcp](), [least-privilege](), [security]() ### AI overview This engineering deep dive describes the DataAgents pattern for analyzing heterogeneous cloud resources at scale. It focuses on cloud resource dormancy detection across AWS, Azure, and Google Cloud Platform, using entity-specific criteria, confidence-based prioritization, and documented reasoning. The article reports reducing the analysis effort from an estimated 6-9 months to 10 days. ### Source excerpt An engineering deep dive into the pattern that changed how we approach large-scale classification problems. Every engineering team has that project sitting in the backlog. The one where someone says, "We really should analyze all of these," and the room goes quiet. Everyone knows what "all of these" means -- hundreds of entities, complex rules, no clear starting point. For us, it was cloud resource dormancy detection. We had around 350 distinct cloud resource types spread across AWS, Azure and Google Cloud Platform (GCP). Each type has different behavior patterns. An EC2 instance sitting idle looks nothing like a dormant Amazon S3 (S3) bucket or an unattached Elastic IP. Detecting dormancy required understanding what "active" means for each specific resource, then writing detection logic that wouldn't flood operations teams with false positives. Traditional estimate: 6-9 months of expert analysis. Actual time: 10 days. Here's how we did it, and more importantly, here's the reusable pattern behind it. The problem with large-scale analysis: Before we get to the solution, it's worth naming the pattern that makes these projects so painful. It shows up everywhere: Cloud resources - Which of our 350 resource types are dormant? Data governance - Which of our 800 tables have quality issues we should monitor? Security - Which of our access entitlements violate least-privilege principles? Compliance - Which of our 500 policy controls need remediation? In every case, the structure is similar -- a large catalog of heterogeneous entities, entity-specific rules that don't generalize, unknown priorities and a high cost for getting it wrong. The traditional approach is not just slow. It's structurally limited. You get coverage of the "obvious" cases, inconsistent logic across analysts, and tribal knowledge that evaporates when people leave. What you need is something that can assess each entity, apply consistent criteria, prioritize by confidence and document its reasoning. The DataA ## System Over Model, Tested: Reproducing Mythos's FreeBSD Find on Local Open-Weight Models DevFeed: [System Over Model, Tested: Reproducing Mythos's FreeBSD Find on Local Open-Weight Models]() Original publisher: [Read original article]() Author: clearbluejar Published: 2026-06-04T07:00:00Z Content type: article Language: en Sources: [clearbluejar]() Topics: [freebsd](), [gpt-oss](), [Artificial Intelligence](), [gemma4](), [bug](), [Large Language Model](), [Benchmark](), [Python]() Tags: [ai](), [benchmark](), [cve](), [false-positives](), [freebsd](), [gemma-4](), [gpt-oss](), [llm](), [llms](), [python](), [rce](), [vulnerability-research]() ### AI overview This article tests AISLE's vulnerability-finding pipeline on the local open-weight models gpt-oss-20b and gemma-4-31b-it using a FreeBSD RCE as the benchmark. The models initially appeared to miss the bug, but a rerun recovered it. The author reports that adding a reachability stage reduced false positives from 30 to 5 while retaining the CVE finding. ### Source excerpt Mythos found a 17-year-old FreeBSD RCE; AISLE reproduced it with gpt-5.4-nano via their nano-analyzer pipeline. I ran the pipeline on two local open-weight models, gpt-oss-20b and gemma-4-31b-it. The misses recovered on re-run. The real problem was the false-positive rate, and one extra system stage cut it from 30 to 5 with the CVE still standing. ## How Split Brain Happens in Distributed Databases and How It Gets Fixed DevFeed: [How Split Brain Happens in Distributed Databases and How It Gets Fixed]() Original publisher: [Read original article]() Published: 2026-03-31T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Databases](), [Network](), [Replication](), [CAP theorem]() Tags: [consensus](), [databases](), [distributed](), [failed](), [false-positives](), [partition](), [replication](), [split-brain]() ### AI overview This tutorial explains how split brain occurs in distributed databases when a network partition causes multiple nodes to become primary and accept conflicting writes. It discusses why timeout-based failure detection can create false positives and introduces the distributed consensus complexity motivated by this failure mode. ### Source excerpt . [How Split Brain Happens in Distributed Databases and How It Gets Fixed](split-brain-in-distributed-databases-cover... ## Bloom Filters vs Counting Bloom Filters: When Deletions Kill Performance DevFeed: [Bloom Filters vs Counting Bloom Filters: When Deletions Kill Performance]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-17T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Algorithms](), [Caching](), [CPU Cache]() Tags: [bloom-filter](), [cache](), [cpu](), [cpu-cache](), [data-structures](), [distributed-systems](), [false-negative](), [false-positives](), [memory](), [performance](), [probabilistic](), [systems]() ### AI overview The article explains that standard Bloom filters support membership checks but not deletion because clearing shared bits can create false negatives. Counting Bloom filters enable deletion with counters, but their larger memory footprint can push lookups out of CPU cache and increase latency through random memory access. ### Source excerpt A standard Bloom filter is append-only because bits are shared between items, so clearing a bit for one item can create a false negative for another. Counting Bloom filters fix deletion by replacing each bit with a 4-bit or 8-bit counter, which multiplies the footprint four to eight times, pushes the structure out of L3, and turns each of the k lookups into a roughly 100 ns RAM hit instead of a 10 ns cache hit. ## How Apollo Built an AI-Powered Style Guide Reviewer for Documentation DevFeed: [How Apollo Built an AI-Powered Style Guide Reviewer for Documentation]() Original publisher: [Read original article]() Author: Daniel Abdelsamed Published: 2026-03-16T12:33:22Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [Artificial Intelligence](), [Documentation](), [AI Development](), [GitHub](), [Caching]() Tags: [ai](), [caching](), [cost](), [docs](), [engineering](), [false-positives](), [github](), [parallelism](), [speed]() ### AI overview Apollo's documentation team built an AI-powered style guide reviewer that applies documentation rules contextually and reports findings to authors in GitHub. The article explains how restructuring the style guide, integrating the review into the existing workflow, and optimizing speed and cost made it practical to run on every commit. ### Source excerpt Learn how Apollo's docs team built an AI-powered style guide reviewer that runs on every commit -- and the three things that made it actually work: restructuring the style guide as a pattern library, integrating results directly into GitHub, and optimizing for speed and cost with caching, line-level review, and parallelism. ## Deploying a Cost-Effective, Scalable PhotoDNA System for CSAM Detection DevFeed: [Deploying a Cost-Effective, Scalable PhotoDNA System for CSAM Detection]() Original publisher: [Read original article]() Author: Anish Kumar Published: 2026-01-20T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [Perceptual hashing](), [observability](), [Machine learning](), [Microsoft]() Tags: [auditability](), [aws](), [content-trust-series](), [cost](), [databricks](), [false-positives](), [featured](), [hashing](), [lambda](), [latency](), [legal](), [microsoft](), [observability](), [privacy](), [reporting](), [scale]() ### AI overview Scribd describes a production system for detecting known Child Sexual Abuse Material using PhotoDNA perceptual hashes. The article covers its integration with NCMEC reporting, ingestion-scale architecture, operational model, cost considerations, and safety-related constraints. ### Source excerpt Child safety is a non-negotiable responsibility for any platform that hosts user-generated content. Over the last year, we designed and deployed a production system that detects known Child Sexual Abuse Material (CSAM) using PhotoDNA perceptual hashes, integrates with the National Center for Missing and Exploted Children's (NCMEC) reporting system, and scales efficiently across our ingestion surfaces. This post explains the problem we set out to solve, how PhotoDNA hashing works, the online child-protection ecosystem (NCMEC, Tech Coalition, Project Lantern), our architecture and operational model, cost considerations, and key learnings. ## Scaling AI Security: How Evo Complements New Agentic Tools DevFeed: [Scaling AI Security: How Evo Complements New Agentic Tools]() Original publisher: [Read original article]() Author: John Carione Published: 2025-11-24T05:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Securing AI](), [Artificial Intelligence](), [Large Language Model](), [Pull Request](), [Testing]() Tags: [agentic](), [agentic-security](), [ai](), [ai-security](), [application-security](), [blog](), [developer](), [false-positives](), [governance](), [interest](), [llm](), [open-source-security](), [openai](), [policy](), [pull-request](), [real-time](), [security]() ### AI overview The article examines OpenAI's Aardvark as an agentic application security tool and evaluates the practical challenges of using it in enterprise development pipelines. It highlights concerns about false positives, inconsistent LLM-based findings, workflow integration, performance, coverage, and limited scope, while presenting Snyk's policy, governance, and real-time intelligence as complementary capabilities. ### Source excerpt OpenAI's Aardvark signals the rise of agentic AppSec, and Snyk shows how policy, governance, and real-time intelligence keep enterprises secure. ## Burp AI takes on a vulnerable web app: watch Tib3rius put Burp's new agentic capabilities to the test DevFeed: [Burp AI takes on a vulnerable web app: watch Tib3rius put Burp's new agentic capabilities to the test]() Original publisher: [Read original article]() Author: Amelia Coen Published: 2025-10-22T12:59:05Z Content type: article Language: en Sources: [PortSwigger Blog]() Topics: [Artificial Intelligence](), [Web app](), [Testing](), [Vulnerabilities]() Tags: [agentic](), [ai](), [blog](), [blog-post](), [exploits](), [false-positives](), [real-time](), [testing](), [video](), [vulnerabilities](), [web-app](), [workflow]() ### AI overview A video demonstrates Burp AI testing a deliberately vulnerable web app. It shows Burp AI autonomously exploiting discovered vulnerabilities, identifying false positives, and using agentic capabilities in Repeater to adapt payloads and approaches through natural-language prompts. ### Source excerpt What happens when you set Burp AI loose on a deliberately vulnerable web app? In his latest video, Tib3rius takes Burp's new agentic Burp AI capabilities for a spin - and the results are seriously coo ## Watch the webinar: Scale secure coverage without scaling headcount DevFeed: [Watch the webinar: Scale secure coverage without scaling headcount]() Original publisher: [Read original article]() Author: Rob Samuels Published: 2025-09-04T14:21:00Z Content type: article Language: en Sources: [PortSwigger Blog]() Topics: [Application Security](), [Testing](), [API](), [Tooling](), [SIEM, Security](), [Access Control]() Tags: [api](), [application-security](), [automated](), [false-positives](), [integrations](), [reporting](), [security]() ### AI overview This promotional developer article presents Burp Suite DAST as a way for application security teams to expand automated security testing across many applications and APIs without increasing headcount. It highlights Burp Suite Professional's scanning engine, familiar workflows, role-based access control, SIEM integrations, reporting, and developer workflow integration. ### Source excerpt Application security teams are under pressure. With expanding application estates, growing API usage, and faster release cycles, many teams struggle to keep up. Backlogs grow, releases are delayed, an ## Always Check Your Tests Against Faulty Inputs DevFeed: [Always Check Your Tests Against Faulty Inputs]() Original publisher: [Read original article]() Published: 2025-07-21T05:19:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [Testing](), [Network]() Tags: [automation](), [blog](), [blog-post](), [false-negative](), [false-positive](), [false-positives](), [netlab](), [network](), [ospf](), [testing](), [tests]() ### AI overview The article explains that tests must be checked with faulty inputs, not only under correct conditions. It uses a netlab integration test for mismatched OSPF network types as an example of a test that could miss the targeted error, and warns that false positives can be more harmful than false negatives. ### Source excerpt A while ago, I published a blog post proudly describing the netlab integration test that should check for incorrect OSPF network types in netlab-generated device configurations. Almost immediately, Erik Auerswald pointed out that my test wouldn't detect that error (it might detect other errors, though) as the OSPF network adjacency is always established even when the adjacent routers have mismatching OSPF network types. I made one of the oldest testing mistakes: I checked whether my test would work under the correct conditions but not whether it would detect an incorrect condition. Read more ... ## Watch the on-demand webinar: Shift left without the strain DevFeed: [Watch the on-demand webinar: Shift left without the strain]() Original publisher: [Read original article]() Author: Rob Samuels Published: 2025-07-14T13:00:00Z Content type: article Language: en Sources: [PortSwigger Blog]() Topics: [Application Security](), [CI/CD](), [Testing](), [Docker](), [GitHub Actions](), [GitLab](), [Jenkins](), [YAML](), [configuration]() Tags: [ci-cd](), [ci-cd-workflows](), [configuration](), [containers](), [docker](), [false-positives](), [github-actions](), [gitlab](), [jenkins](), [security](), [shift-left](), [software-delivery](), [yaml]() ### AI overview This article promotes an on-demand webinar about shifting application security left without slowing software delivery. It discusses the challenges of integrating DAST into CI/CD workflows, including slow scans, false positives, and workflow friction, and presents Burp Suite DAST as a fast, configurable, Docker-based solution that integrates with common pipeline tools. ### Source excerpt Shifting security left promises faster, safer software delivery - but for many teams, that promise is undercut by painful scan performance, false positives, and pipeline friction. In our recent webina ## Minimizing False Positives: Enhancing Security Efficiency DevFeed: [Minimizing False Positives: Enhancing Security Efficiency]() Original publisher: [Read original article]() Author: Tiago Mendo Published: 2025-07-01T23:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [threat detection](), [Cybersecurity](), [Detection engineering](), [Vulnerabilities](), [data]() Tags: [2025](), [application-security](), [awareness](), [blog](), [cybersecurity](), [data](), [developer](), [devops](), [efficiency](), [engineering](), [false-positives](), [organizational](), [reduce](), [security](), [security-tools](), [threat-detection](), [vulnerabilities]() ### AI overview The article explains how false-positive security alerts consume investigation time, contribute to alert fatigue, and delay responses to genuine cyberattacks. It contrasts false positives and false negatives with accurate true-positive detection, emphasizing the value of security tools that improve accuracy and help organizations respond to real threats efficiently. ### Source excerpt Discover how enhanced security tools reduce false positives and streamline threat detection for more effective cybersecurity management. ## Trusted Open Source Means Compatibility: New Integration with Orca Security DevFeed: [Trusted Open Source Means Compatibility: New Integration with Orca Security]() Original publisher: [Read original article]() Published: 2025-06-17T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [chainguard containers](), [orca security](), [Security](), [container images](), [Containers]() Tags: [chainguard-containers](), [chainguard-os](), [compatibility](), [false-positives](), [open-source-scanners](), [orca-security](), [partnership](), [provenance](), [scanner](), [scanners](), [security](), [vulnerabilities]() ### AI overview Chainguard announces an integration with Orca Security that adds visibility for Chainguard OS and Chainguard Containers images in the Orca Platform. The integration exposes image metadata, scans images and installed packages for vulnerabilities against Chainguard Security Advisories, and provides additional security context for remediation. ### Source excerpt Chainguard now has an integration with Orca Security. Discover more about our new partnership. ## Suppressions of Suppressions DevFeed: [Suppressions of Suppressions]() Original publisher: [Read original article]() Published: 2025-06-11T00:00:00Z Content type: article Language: en Sources: [Dan Abramov]() Topics: [Code](), [ci](), [syntax](), [Open Source]() Tags: [ci](), [code](), [false-positives](), [lint](), [linter](), [open-source](), [rules]() ### AI overview The article examines lint-rule suppressions: they can handle false positives and gradual rule adoption, but suppressing certain rules may cause serious problems. It proposes a lint rule that prevents suppression of selected other rules and notes that this protection can itself be suppressed. ### Source excerpt I heard you like linting. ## Revamping Data Science Interviews DevFeed: [Revamping Data Science Interviews]() Original publisher: [Read original article]() Author: Daeus Jorento Published: 2025-05-28T16:00:00Z Content type: article Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [Data Science](), [Statistics](), [data-engineering](), [Machine learning](), [Generative AI]() Tags: [analytics](), [chatgpt](), [data-engineering](), [data-science](), [false-positives](), [generative-ai](), [hiring](), [interviews](), [machine-learning]() ### AI overview This article explains why data science organizations periodically revamp their interview processes. It covers changing business needs, broader problem banks, candidate experience and reputation, and the impact of generative AI tools such as ChatGPT on syntax-heavy technical questions. ### Source excerpt Interviews are not just about improving hiring outcomes - they are about strengthening the entire DS function ## Panic! At The Distro: A Study of Malware Prevention in Linux Distributions DevFeed: [Panic! At The Distro: A Study of Malware Prevention in Linux Distributions]() Original publisher: [Read original article]() Published: 2024-12-17T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Linux](), [Malware](), [Maintainers](), [open-source-security](), [Benchmark](), [dataset]() Tags: [alpine](), [benchmark](), [dataset](), [debian](), [distribution](), [false-positives](), [linux](), [maintainers](), [malware](), [malware-prevention](), [open-source-software](), [reproducible-builds](), [research](), [scanners](), [software-supply-chain](), [wolfi]() ### AI overview Chainguard studies malware prevention in Linux distributions through maintainer interviews and a Linux package malware benchmark dataset. The study reports that most interviewed distributions do not proactively scan repositories, while existing open-source malware scanners often produce false positives. ### Source excerpt Chainguard wanted to know more about malware prevention in Linux distributions. So we did a study to see what maintainers are doing about it. See the results. [Next page]()