AI Supply Chain Security
for Complete AI Risk Control

Checkmarx One AI Supply Chain Security discovers LLMs, AI SDKs, AI Libraries, MCP Servers. MCP Clients, AI Agents within your application.  

You can explore all Checkmarx’s  documentation here 

No, our discovery engine is deterministic and relies on real signals, analyzing source code, dependency files, configuration manifests, and import statements, not AI inference. 

Every organization has unique needs and sizes. For a price quote, please get in touch. See our packaging here.  

 If you are a current Checkmarx customer, please reach out to your account manager or contact us here 

Checkmarx provides dedicated security assessment scanners for LLMs and MCPs. For LLMs, we detect security risks like insecure deserialization, dangerous model loaders, shell execution, and suspicious pickle/torch gadget patterns. 

AI supply chain security is the practice of identifying and mitigating risks introduced by AI components including open-source LLMs, ML frameworks, pre-trained models, and AI-generated code, across the software development lifecycle. Unlike traditional supply chain risks, AI components can introduce hidden vulnerabilities through poisoned training data, compromised model weights, or insecure integrations that are difficult to detect with conventional security tooling. 

s AI Software Supply Chain Security identifies AI components across your applications -models, LLMs, MCP servers – providing visibility for compliance frameworks. It helps determine which AI systems fall under EU AI Act risk classifications, NIST AI RMF governance, and ISO 42001 standards . 

AI agents introduce security risks including excessive privilege, insecure tool invocation, indirect prompt injection via external data sources, and unintended data exfiltration. These risks are amplified in multi-agent or autonomous workflow environments, where a single compromised agent can propagate malicious instructions across an entire pipeline with little human oversight.

Start by taking an inventory of all AI usage to uncover Shadow AI. Assess your maturity with Checkmarx APMA. Align to frameworks like NIST AI RMF or ISO 42001. Define roles: AI risk owners, validators, compliance reviewers. Use AI SCS for continuous discovery and policy enforcement. 

Securing open-source AI models and ML frameworks requires continuous inventory of all AI components in use, integrity verification of model weights and dependencies, and runtime monitoring for anomalous behavior. Organizations should treat every open-source model as an untrusted third-party dependency, scanning for known vulnerabilities, verifying provenance, and enforcing least-privilege access before deployment.