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5 posts

• ML Model Provenance: Signing, SBOMs, and Verifying the AI You Deploy Before It Runs

  You wouldn't deploy software without checksums and signatures. But most organizations download model weights and run them without any provenance verification at all. This post covers the practical mechanics of model signing, ML SBOMs, and the emerging infrastructure for verifying a model's origins before it touches production.

  Jul 6, 2026 supply-chain-security model-security defense-patterns content-provenance

• Malicious AI Model Files: Pickle Exploits and Arbitrary Code Execution on Model Load

  Downloading a model file and calling torch.load() is a potential code execution event. This post explains the pickle mechanics that make it so, the real-world exploits already found in the wild, how HuggingFace has responded, and what safe alternatives actually prevent the attack.

  Jul 5, 2026 supply-chain-security model-security deserialization pickle pytorch safetensors defense-patterns

• Trojan Triggers in Multi-Modal Models: How Visual Backdoors Activate Hidden Behaviors in Vision-Language Systems

  A small adversarial patch embedded in an image — invisible or benign-looking to humans — can reliably trigger hidden behaviors when a vision-language model processes it. As VLMs get deployed in agentic pipelines, visual backdoors become a practical production threat.

  Jul 4, 2026 backdoor-attacks vision-language-models multimodal adversarial-ml supply-chain-security agentic-ai trojan

• Backdoor Attacks in Foundation Models: Sleeper Triggers That Survive Fine-Tuning

  Pre-trained LLMs can be trojaned at the foundation stage, with adversarial triggers embedded in weights that persist through downstream fine-tuning and RLHF safety training. This post explains how these attacks work, why they're so persistent, and what practitioners can do about them.

  Jul 3, 2026 backdoor-attacks supply-chain-security adversarial-ml fine-tuning model-security trojan

• Tool Poisoning via Malicious MCP Servers: When Your Agent's Tools Turn Against It

  MCP servers are the extension layer for modern AI agents — granting file access, web search, code execution, and API calls. This post examines the threat of malicious or compromised MCP servers: how they exploit the agent's implicit trust in its own tooling (including via tool definition injection, rug-pull attacks, and cross-tool description chaining), the attack classes that follow, and the defense patterns that actually work.

  Jun 29, 2026 mcp-security tool-use agent-security supply-chain threat-modeling defense-patterns prompt-injection agentic-ai ai-security supply-chain-security