# ai code security Published articles for ai code security. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Capital One Announces Open-Source VulnHunter Agentic AI Code Security Tool DevFeed: [Capital One Announces Open-Source VulnHunter Agentic AI Code Security Tool]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-07-17T17:01:41Z Content type: release Language: en Sources: [Capital One Tech]() Topics: [open-source-security](), [code security](), [Artificial Intelligence](), [Open Source](), [Vulnerabilities](), [Tooling](), [Developer experience]() Tags: [agentic-ai-security](), [ai-code-security](), [ai-security](), [build](), [claude-code](), [code-vulnerability](), [developer](), [developer-experience](), [developers](), [exploit](), [generative-ai-tools](), [open-source](), [vulnerabilities]() ### AI overview Capital One announces the open-source release of VulnHunter, an agentic AI security tool that analyzes source code from an attacker's perspective. It is designed to identify potentially exploitable defects, map prospective attack paths, and propose targeted code remediations. ### Source excerpt Capital One's open-source, agentic AI code security tool. The rules of software security are changing faster than most defenders can keep pace. Advanced AI models have dramatically lowered the barrier for bad actors to discover and exploit vulnerabilities in software. What once required significant skill and time can now be automated, accelerated, and scaled. The world faces an increasingly short window of time before highly sophisticated, next-generation AI attack capabilities become affordable and accessible to virtually every adversary. Across the industry, organizations are racing to prepare for this paradigm shift. Traditional environmental protections like network segmentation, identity controls, and monitoring remain essential, but are no longer sufficient on their own. The ultimate defense in this new reality requires a shift in approach: organizations need to consider and detect the vulnerabilities in their code and fix them before adversaries can deploy advanced models to discover and exploit them. At Capital One, we decided that the right response to AI-enabled threats wasn't to wait, but to build cutting-edge AI-driven defenses and put them in the hands of defenders everywhere. That's why we are announcing today the open-source release of VulnHunter, an advanced agentic AI security tool designed to apply proactive, attacker-perspective analysis directly to the source code. Developed internally at Capital One, VulnHunter is not a traditional, passive vulnerability scanner. It represents a shift in defensive tooling with an agentic reasoning workflow to identify potentially exploitable defects, map prospective attack paths, and propose highly targeted code remediations. Built for the developer experience To fully unlock the utility of VulnHunter, we knew ease of use mattered. A persistent challenge with traditional security tools is that they are often built primarily to enforce rigid cybersecurity practices, without much consideration for a developer's ac ## Ship and patch doesn't cut it in the AI era DevFeed: [Ship and patch doesn't cut it in the AI era]() Original publisher: [Read original article]() Published: 2026-04-08T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [supply-chain-security](), [Security](), [prompt injection](), [GitHub Actions](), [litellm](), [trivy]() Tags: [ai](), [ai-code-security](), [ai-coding](), [ai-security](), [chainguard-containers-for-ai](), [chainguard-libraries-for-ai](), [github-actions](), [litellm](), [prompt-injection](), [security](), [software-supply-chain](), [software-supply-chain-security](), [trivy]() ### AI overview This opinion article argues that AI-generated code and dependencies are entering software environments faster than traditional security review processes can handle, while AI also accelerates vulnerability discovery and attacks. It concludes that reactive scanning and patching alone are insufficient and emphasizes trusted inputs for software supply-chain security. ### Source excerpt AI is accelerating code and attacks. Learn why patching alone can't keep up, and why securing the software supply chain starts with trusted inputs. ## A developer's best friend: Lessons learned from our canine companions about AI code security DevFeed: [A developer's best friend: Lessons learned from our canine companions about AI code security]() Original publisher: [Read original article]() Author: Krysta Williams-Timm Published: 2024-08-26T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [Application Security](), [Generative AI](), [Vulnerabilities](), [Pull Request]() Tags: [ai-code-security](), [application-security](), [blog](), [code-security](), [developer](), [generative-ai](), [megawatt](), [pull-request](), [security-vulnerabilities](), [snyk]() ### AI overview This article uses the metaphor of a loyal guard dog to explain how AI security companions can help developers identify and fix vulnerabilities in AI-generated code. It emphasizes addressing security issues as soon as code enters a pull request, before they move further through development workflows. ### Source excerpt In honor of this year's International Dog Day, we've put together three lessons that we can learn from our four-legged friends about AI code security. ## Snyk & Atlassian: How to embed security in AI-assisted software development DevFeed: [Snyk & Atlassian: How to embed security in AI-assisted software development]() Original publisher: [Read original article]() Author: LaToya Muff Published: 2024-02-14T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [AI-assisted coding](), [AI Development](), [sdlc](), [Large Language Model](), [Vulnerabilities](), [atlassian](), [snyk]() Tags: [acquisition](), [ai](), [ai-code-security](), [ai-coding](), [application-security](), [atlassian](), [awareness](), [blog](), [code-security](), [developer](), [devops](), [github-copilot](), [language-models](), [megawatt](), [sdlc](), [security](), [snyk](), [snyk-code](), [vulnerabilities]() ### AI overview This article discusses how AI-assisted software development creates productivity opportunities while introducing security risks. It describes common uses such as code generation, code summarization, documentation, refactoring, templates, and pair programming, and identifies bad training data and hallucinations as primary causes of issues. The article recommends embedding trust and security throughout the software development life cycle. ### Source excerpt Developers are using AI, and there's no turning back. Here's how you can minimize vulnerabilities and security risks associated with AI coding tools. ## How to choose a security tool for your AI-generated code DevFeed: [How to choose a security tool for your AI-generated code]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2024-01-09T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [code security](), [snyk-code](), [snyk](), [ai-coding](), [Generative AI](), [real-time]() Tags: [acquisition](), [ai](), [ai-code-security](), [blog](), [code-security](), [developer](), [guide](), [ide](), [learn](), [pmm](), [real-time](), [security](), [snyk](), [snyk-code]() ### AI overview This buyer's guide presents five considerations for choosing a security tool for AI-generated code. It emphasizes real-time analysis within the IDE, minimal disruption to developer workflows, speed that keeps pace with AI coding tools, and accuracy that helps address generative AI hallucinations. The article uses Snyk's experience and Snyk Code, powered by DeepCode AI, as examples. ### Source excerpt There are a multitude of considerations when it comes to picking the right security companion for your preferred generative AI tool. Here are the top 5 factors for you to consider when making your selections. ## Snyk highlights AWS re:Invent 2023 DevFeed: [Snyk highlights AWS re:Invent 2023]() Original publisher: [Read original article]() Author: David Lugo Published: 2023-12-14T06:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Generative AI](), [Securing AI](), [Amazon Web Services](), [ai security](), [code security](), [Application Security](), [Application Development](), [Language models](), [cloud security](), [Infrastructure as code](), [Containers](), [sdlc]() Tags: [acquisition](), [ai](), [ai-code-security](), [ai-coding](), [ai-security](), [ai-tools](), [amazon](), [application-development](), [aspm](), [awareness](), [aws](), [blog](), [cloud](), [cloud-computing](), [code](), [code-security](), [coding](), [container-security](), [containers](), [devops](), [executive](), [generative-ai](), [iac-security](), [interest](), [megawatt](), [related-content](), [sdlc](), [security](), [security-solutions](), [snyk](), [snyk-apprisk](), [snyk-container](), [snyk-iac](), [tool]() ### AI overview This recap covers Snyk's AWS re:Invent 2023 highlights, including AWS partnership achievements, new AWS integrations, generative AI developments, and the use of AI coding assistants. It emphasizes the need for AI security solutions because AI-generated code can increase vulnerabilities. ### Source excerpt In this blog, we recap the most exciting developments from AWS re:Invent 2023, including Snyk's industry-leading progress with generative AI, ASPM, and the importance of AI security.