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A small case study on poisoning in agent procedural memory shows that a 'proof before reuse' rule—serving a learned skill only after two later sessions reproduce it, showing only the reproduced lines—keeps poisoned skills out of memory (0/30 exposure vs 3/30 for naive memory and 30/30 for replay), with all data, code, and per-session records released.
Discussion about whether ML teams are actually testing model security risks like extraction and poisoning in production, noting that security review for models lags behind regular software.
POISE is a stealthy skill-poisoning attack that embeds malicious triggers within benign-looking instructions, achieving high attack success rates while evading detection by LLM scanners.