# chainguard for ai Published articles for chainguard for ai. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Preparing for Mythos: Practical advice for engineering teams DevFeed: [Preparing for Mythos: Practical advice for engineering teams]() Original publisher: [Read original article]() Published: 2026-05-26T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [ai security](), [Security](), [anthropic](), [exploit chaining](), [Vulnerabilities](), [open-source-security](), [cURL]() Tags: [advice](), [ai](), [ai-security](), [ai-vulnerability-remediation](), [anthropic](), [article](), [chainguard-containers](), [chainguard-for-ai](), [chainguard-libraries](), [engineering](), [exploit-chaining](), [exploits](), [mythos](), [secure-by-default](), [security](), [vulnerabilities](), [work]() ### AI overview This opinion assesses the security implications of Anthropic's Mythos model and argues that defenders should use AI-assisted methods to identify and remediate vulnerabilities. It says Mythos appears especially capable at exploiting vulnerabilities and chaining exploits, while a test on curl found one new non-critical vulnerability. ### Source excerpt Anthropic's Mythos raises the stakes for software security. Learn how to survive faster exploits with secure-by-default supply chains and AI-assisted defense. ## 2026: The year of AI-assisted attacks DevFeed: [2026: The year of AI-assisted attacks]() Original publisher: [Read original article]() Published: 2026-04-14T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [Cybercrime](), [Hacking](), [Large Language Model](), [Claude Code](), [ChatGPT](), [Malware]() Tags: [ai](), [ai-assisted-attacks](), [ai-containers](), [axios](), [breach](), [chainguard-for-ai](), [chatgpt](), [claude-code](), [code](), [cybercrime](), [llm](), [malicious-packages](), [malwhere](), [mythos](), [phishing](), [telnyx](), [trivy]() ### AI overview The article argues that AI-assisted attacks became more capable and accessible in 2025. It describes how ChatGPT, Claude Code, and other LLM-backed systems enabled nontechnical individuals and lone actors to conduct attacks previously associated with skilled hackers or organized teams, while citing increases in malicious packages, cloud intrusions, and AI-generated phishing. ### Source excerpt AI is lowering the barrier to cybercrime. Learn why attacks are rising fast, and how eliminating entire classes of supply chain risk is the only path forward. ## AI is finding vulnerabilities faster than anyone can patch them. Now what? DevFeed: [AI is finding vulnerabilities faster than anyone can patch them. Now what?]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [Vulnerabilities](), [Artificial Intelligence](), [supply-chain-security](), [Claude](), [anthropic](), [Maintainers](), [Open Source](), [Exploit](), [Operating system](), [browser]() Tags: [ai](), [anthropic](), [attacks](), [chainguard-for-ai](), [claude](), [claude-mythos-preview](), [cves](), [exploit](), [maintainers](), [open-source](), [project-glasswing](), [security](), [software-supply-chain](), [software-supply-chain-security](), [supply-chain-attacks](), [vulnerabilities]() ### AI overview The article argues that AI systems such as Claude Mythos Preview are discovering zero-day vulnerabilities faster than organizations and open-source maintainers can patch them. It describes the resulting risks for software supply chains, including faster exploitation, uneven vendor response, and increased pressure on maintainers, while presenting verifiable-source, secure-by-default artifacts as a defensive approach. ### Source excerpt Project Glasswing and Claude Mythos Preview reveal a surge in zero-days. Learn why reactive patching fails and how secure-by-default supply chains keep you safe. ## Protect your AI workloads from supply chain attacks DevFeed: [Protect your AI workloads from supply chain attacks]() Original publisher: [Read original article]() Published: 2026-01-30T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [Containers](), [chainguard](), [Vulnerabilities](), [AI Strategy](), [Open Source](), [Security](), [PyTorch](), [Tensorflow](), [MLOps]() Tags: [ai](), [ai-containers](), [chainguard](), [chainguard-containers](), [chainguard-for-ai](), [chainguard-libraries](), [containers](), [cudnn](), [cves](), [data-processing](), [deployment](), [kserve](), [legacy](), [libraries](), [mlops](), [nemo](), [ollama](), [python](), [pytorch](), [security](), [security-vulnerabilities](), [supply-chain](), [supply-chain-attacks](), [zero-cve-containers]() ### AI overview The article discusses security and operational challenges in AI/ML workloads, including complex dependencies, bloated artifacts, infrastructure sprawl, and unremediated CVEs. It presents Chainguard Containers' minimal images for AI workloads as a way to reduce attack surface, storage needs, and deployment overhead, and cites a 50 MB gpu-operator image compared with a 170 MB upstream equivalent. ### Source excerpt Chainguard secures AI adoption with minimal, zero-CVE containers and source-built libraries that prevent supply chain malware while keeping developers fast.