DoubtProbe: Black-Box Jailbreak Defense via Structural Verification and Semantic Auditing

• URL: http://arxiv.org/abs/2606.16527v1
• Date: Mon, 15 Jun 2026 10:30:29 GMT
• Title: DoubtProbe: Black-Box Jailbreak Defense via Structural Verification and Semantic Auditing
• Abstract summary: Black-box jailbreak defense has become an important practical problem.<n>We propose DoubtProbe, a dual-branch inference-time defense framework.<n>We evaluate DoubtProbe against representative black-box defenses on jailbreak and benign-request benchmarks.
• Score: 16.91220273436945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) are increasingly deployed in user-facing systems, black-box jailbreak defense has become an important practical problem. Existing defenses often rely on known-attack coverage, prompt-level semantic judgment, or local runtime control, yet these paths can become unstable under evolving prompt packaging, expression rewriting, and structure manipulation. We observe that many black-box jailbreaks do not remove the harmful goal, but reorganize the information needed to express and execute it, thereby evading safety alignment while remaining recoverable during generation. Motivated by this observation, we propose DoubtProbe, a dual-branch inference-time defense framework that combines structural verification with semantic auditing and formulates black-box jailbreak defense as consistency checking under controlled transformation. The structural branch extracts a structured representation from the original request, reconstructs the request under representation constraints, and detects information-preservation failures between the original and reconstructed requests; the semantic branch audits the original prompt directly. We evaluate DoubtProbe against representative black-box defenses on jailbreak and benign-request benchmarks, and further test backbone transfer from Qwen2.5-72B to Llama-3.1-70B. Results show that DoubtProbe achieves a stronger and more stable defense-utility trade-off: on Qwen2.5-72B, it reduces the JBB attack success rate from 0.293 to 0.100 and the CodeAttack attack success rate from 0.152 to 0.001, while maintaining false positive rates of 0.022 and 0.016 on AlpacaEval and OR-Bench; the same pattern remains stable on Llama-3.1-70B. These findings show that structural inconsistency signals provide a practical and generalizable basis for black-box jailbreak defense, especially when combined with semantic auditing.

Related papers

• AlcaTRAz - Anchored Tree-Rule Defense Against Jailbreaks [0.08699280339422538]
  Large language models (LLMs) are vulnerable to jailbreak attacks that bypass safety alignment through carefully crafted prompts.<n>We propose AlcaTRAz, a prompt-level defense based on rule trees that operates exclusively on the input text.<n>We evaluate the proposed method across 33 open-weight models, 22 jailbreak attack types, and a benchmark of short, single-turn benign questions.
  arXiv  Detail & Related papers  (2026-09-03T11:30:08Z)
• SafeCA: Safe Cross-Attention Localization and Regulation for Text-to-Video Jailbreak Defense [77.70598852234149]
  Text-to-Video (T2V) generative models are vulnerable to jailbreak attacks in real-world deployment.<n>We propose SafeCA, a feature-level defense mechanism for safe cross-attention localization and regularization.<n> Experimental results show that SafeCA reduces the jailbreak success rate by about 20% on mainstream T2V models.
  arXiv  Detail & Related papers  (2026-08-11T14:01:24Z)
• Defending Jailbreak Attacks on Large Language Models via Manifold Trajectory Kinetics [50.36375380196006]
  Jailbreak prompts can bypass alignment guardrails in large language models.<n>Prior detection approaches largely rely on a fixed metric space.<n>We show this assumption breaks under (i) pseudo-malicious prompts that are benign by intent but contain safety-related keywords.<n>We present Manifold Trajectory Kinetics (MTK), which treats an LLM as a kinetic system transforming inputs into outputs.
  arXiv  Detail & Related papers  (2026-06-05T14:49:26Z)
• Revisiting JBShield: Breaking and Rebuilding Representation-Level Jailbreak Defenses [1.5927806443377646]
  We present JB-GCG, which modifies GCG's objective to combine two terms: refusal-direction suppression via cosine similarity and toxic-concept regularization.<n>Across five configurations on Llama-3-8B, JB-GCG achieves an average ASR of 46.2%, reaching up to 53.4% in the strongest setting.<n>We introduce Trajectory Verification (RTV), a new defense based on Mahalanobis outlier detection over multi-layer refusal-direction fingerprints.
  arXiv  Detail & Related papers  (2026-05-04T19:17:50Z)
• TrajGuard: Streaming Hidden-state Trajectory Detection for Decoding-time Jailbreak Defense [22.678364306206493]
  Existing jailbreak defense paradigms rely on static detection of prompts, outputs, or internal states.<n>We show that hidden states in critical layers during decoding carry stronger and more stable risk signals than input jailbreak prompts.<n>We propose TrajGuard, a training-free, decoding-time defense framework.
  arXiv  Detail & Related papers  (2026-04-09T02:22:44Z)
• Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models [2.6140509675507384]
  We study jailbreaking from both security and interpretability perspectives.<n>We propose a tensor-based latent representation framework that captures structure in hidden activations.<n>Our results provide evidence that jailbreak behavior is rooted in identifiable internal structures.
  arXiv  Detail & Related papers  (2026-02-12T02:43:17Z)
• TrapSuffix: Proactive Defense Against Adversarial Suffixes in Jailbreaking [52.72486831074384]
  Suffix-based jailbreak attacks append an adversarial suffix, i.e., a short token sequence, to steer aligned LLMs into unsafe outputs.<n>We propose TrapSuffix, a lightweight fine-tuning approach that injects trap-aligned behaviors into the base model without changing the inference pipeline.<n>Across diverse suffix-based jailbreak settings, TrapSuffix reduces the average attack success rate to below 0.01 percent and achieves an average tracing success rate of 87.9 percent.
  arXiv  Detail & Related papers  (2026-02-06T11:43:56Z)
• ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplification [47.135407245022115]
  Existing detection methods mainly detect jailbreak status relying on jailbreak templates present in the training data.<n>We propose a layer-wise, module-wise, and token-wise amplification framework that progressively magnifies internal feature discrepancies between benign and jailbreak prompts.<n>Building upon these insights, we introduce ALERT, an efficient and effective zero-shot jailbreak detector.
  arXiv  Detail & Related papers  (2026-01-07T05:30:53Z)
• JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification [18.505062396846565]
  We show that Large Language Models (LLMs) often fail against jailbreak attacks.<n>We propose $textbfJ$ailbreak $textbfP$ath $textbfU$nlearning (JPU) to rectify dynamic jailbreak paths towards safety anchors.
  arXiv  Detail & Related papers  (2026-01-06T13:30:10Z)
• Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models [80.66766532477973]
  Test-time IMmunization (TIM) can adaptively defend against various jailbreak attacks in a self-evolving way.<n>Test-time IMmunization (TIM) can adaptively defend against various jailbreak attacks in a self-evolving way.
  arXiv  Detail & Related papers  (2025-05-28T11:57:46Z)
• AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens [83.08119913279488]
  We present a systematic analysis of the dependency relationships in jailbreak attack and defense techniques. We propose three comprehensive, automated, and logical frameworks. We show that the proposed ensemble jailbreak attack and defense framework significantly outperforms existing research.
  arXiv  Detail & Related papers  (2024-06-06T07:24:41Z)
• JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models [123.66104233291065]
  Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address. JailbreakBench is an open-sourced benchmark with the following components.
  arXiv  Detail & Related papers  (2024-03-28T02:44:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.