Security tools still catching up to AI supply chain risks: “Most tools today aren’t fully equipped to scan AI models or prompts for malicious code, and attackers are already exploiting that gap,” Sonatype’s Fox says. “While some early solutions are emerging, organizations shouldn’t wait. They need to extend existing security policies to cover these new components now, because the risk is real and growing.”Ken Huang, CAIO of DistributedApps.ai and co-chair of the Cloud Security Alliance (CSA)’s AI Safety Working Group, concurs: “Teams often prioritize speed and innovation over rigorous vetting, especially as vibe coding makes it easier to generate and share code rapidly. This environment fosters shortcuts and overconfidence in AI outputs, leading to the integration of insecure or unverified components and increasing the likelihood of supply chain compromise.”Vibe coding is the increasingly common practice of developing entire applications with the help of LLM-powered code assistants, with the human acting as the overseer giving input through natural language prompts. Security researchers have warned that this practice can result in code with hard-to-detect errors and vulnerabilities.The CSA, a nonprofit industry association that promotes security assurance practices in cloud computing, recently published an Agentic AI Red Teaming Guide co-authored by Huang together with more than 50 industry contributors and reviewers. One of the chapters tackles testing for AI agent supply chain and dependency attacks that can lead to unauthorized access, data breaches, or system failures.
A comprehensive MLSecOps approach: “Dependency scanners, lockfiles, and hash verification help pin packages to trusted versions and identify unsafe or hallucinated dependencies.” Huang tells CSO. “However, not all threats, such as subtle data poisoning or prompt-based attacks, are detectable via automated scans, so layered defenses and human review remain critical.”Huang’s recommendations include:
Vibe coding risk mitigation: Recognize that vibe coding can introduce insecure or unnecessary dependencies, and enforce manual review of AI-generated code and libraries. Encourage skepticism and verification of all AI-generated suggestions, especially package names and framework recommendations.MLBOM and AIBOM: Establishing a machine learning or AI bill of materials will provide enterprises with detailed inventories of all datasets, models, and code dependencies, offering transparency and traceability for AI-specific assets. Model cards and system cards help document intended use, limitations, and ethical considerations, but do not address the technical supply chain risks. MLBOM/AIBOM complements these by focusing on provenance and integrity.Continuous scanning and monitoring: Integrate model and dependency scanners into CI/CD pipelines, and monitor for anomalous behaviors post-deployment.Zero trust and least privilege: Treat all third-party AI assets as untrusted by default, isolate and sandbox new models and agents, and restrict permissions for AI agents.Policy alignment: Ensure that AI platforms and repositories are covered by existing software supply chain security policies, updated to address the unique risks of AI and vibe coding.
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