The asymmetry problem: AI safeguards are mainly annoying to the good guys
This did not work: the analysis requires submitting large volumes of real attack commands, exploit payloads, and C2 artifacts, and these requests were blocked by the providers' safety guardrails, which cannot distinguish an incident responder from an attacker. We ran the forensic analysis instead on GLM 5.2, an open-weight model, on our own infrastructure. This had a second benefit: no attacker data, and none of the credentials it referenced, left our environment.This experience points to a gap worth planning for.
- ▪This did not work: the analysis requires submitting large volumes of real attack commands, exploit payloads, and C2 artifacts, and these requests were blocked by the providers' safety guardrails, which cannot distinguish an incident respond
- ▪We ran the forensic analysis instead on GLM 5.2, an open-weight model, on our own infrastructure.
- ▪This had a second benefit: no attacker data, and none of the credentials it referenced, left our environment.This experience points to a gap worth planning for.
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Jot The asymmetry problem : AI safeguards are mainly annoying to the good guys Morgan Hotonnier 20 Jul 2026 — 2 min read Share HuggingFace just experienced a very common problem with cybersecurity safeguards in general audience tooling : it only hinders the good guys on the defence side.In their July 2026 Security incident disclosure, when they tried to leverage their AI-assisted tool set for forensics analysis, they quickly hit a brick wall due to the model provider's safeguard :When we started the log analysis, we first used frontier models behind commercial APIs.
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