JailBound: FOL-Guided Jailbreak Evaluation for Exploring LLM Behavioral Boundaries

Summary

JailBound is a jailbreak evaluation framework that builds its benchmark automatically with a fine-tuned meta-attack generator and searches attacks with first-order loss (FOL)-guided dual-branch search. Tested on 46 LLMs from 13 model families, its High-Value Branch improves average ASR over a ZOL-only baseline by 5.1 and 4.9 percentage points across two settings. Its Safety-Sensitivity Branch shows higher behavioral flip rates than the High-Value Branch under local perturbations, indicating greater local sensitivity of judge-assigned safety labels.

Topics

• Prompt injection and jailbreaks

Related items

• MediumCPython flaw exposes AI inference servers to sandbox escape attacksSimilar attack · CSO Online
• MediumCVE-2026-88979: Kubeflow Pipelines TensorBoard proxy forgeable token via default HMAC secretSimilar attack · NVD/CVE Database
• MediumResearchers Bypass ChatGPT Lockdown Mode to Exfiltrate Chats Without User InteractionSimilar attack · The Hacker News
• InfoEscalate or Close: How Fast-Tier Models Handle Held-Out Web Attack Families in Two-Tier TriageSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• MediumChatGPT Lockdown Mode Bypass: Phishing, Malware Delivery, and Silent Data ExfiltrationSimilar attack · Check Point Research