Failure-Aware Penetration Testing via Typed Failure Tokens: From Calibrated Prediction to Structured Recovery

This work develops a failure-aware approach to penetration testing with language-model agents. It introduces typed failure tokens that turn calibrated predictions of likely failures into structured recovery signals, helping agents identify unsuccessful actions, select appropriate recovery strategies, and continue multi-step security testing more reliably.