Activation Probes Surface Code-Security Signals that the Model's Output Misses
AI coding agents now write a growing share of production code, and human security review does not scale at the rate code is generated. The agents in widest use are closed-weight, so a deploying team cannot read their internals. It can instead run an open-weight model as a reviewer over the agent's output. That reviewer's activations are readable. We ask whether reading those activations recovers a security signal that simply asking the same reviewer misses. We fit a single linear probe per model on a corpus of paired vulnerable-and-fixed Python functions, then test it without retraining on real disclosed vulnerabilities whose weakness type the probe never saw in training, across five open...
| Authors | Ivan Wiryadi |
|---|---|
| Primary category | cs.CR |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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