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First reported today binarly.io

Malicious Model Detection with MLTracer Syscalls

Binarly researchers present MLTracer, a pipeline that detects malicious ML model files through dynamic analysis of system-call traces captured in a sandbox, labeling load-time behavior with syscall categories and a fine-tuned Sentence Transformers embedding model. Applied at scale to Hugging Face, the study found major platform scanners miss over 20% of models that trigger unintended code execution and systematizes the static-scanner evasion techniques behind those misses.

First reported yesterday pluto.security

Can MCP Security Scanners Miss Malicious Servers?

Pluto Research tested five public MCP security scanners (Cisco mcp-scanner, Snyk agent-scan, Ventrova sentinel-scan-cli, mcp-shield, and NVIDIA SkillSpector) against two working malicious MCP servers — a credential thief that exfiltrated SSH, AWS, and Kubernetes credentials and a remote-code-execution server — both disguised as ordinary weather tools. Each scanner returned a clean or zero-finding result in at least one tested configuration; for example, SkillSpector's LLM review skipped files after a 16 MiB size limit, and metadata-only scanners inspected only tool names, descriptions, and schemas. The team submitted four upstream fixes.

First reported 30 Jul adversa.ai

A hole in every one: bypassing the open source AI skill scanners

Adversa's Omer Ben Simon tested eight open source AI agent-skill scanners (including Cisco, NVIDIA, mondoo, skillcop, claude-av, huifer, ai-skill and the OASB leaderboard leader HMA) against real attacks and got a malicious skill past all eight. The shared flaw is a missing front end: scanners match the literal bytes in a SKILL.md file rather than decoding, normalizing Unicode, or re-scanning executed payloads, so encoding, homoglyphs, paraphrasing, reassembly and even framed injections into an LLM judge prompt bypass detection.

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