mlsanityData to 7 Oct 2026

The Fragility of Jailbreak Robustness Across Operational States

arXiv 2608.30748 ↗First posted 31 Aug 2026cs.CR cs.CLcited by 0

On the map: LLM safety and security → Jailbreaking

Abstract (arXiv)

Existing jailbreak evaluations typically characterize robustness using a single attack success rate (ASR) measured in a default configuration (the vanilla state). However, user-LLM interactions can induce diverse operational states beyond the vanilla state. In this work, we find that jailbreak robustness is highly fragile to operational-state variation: even when the attack remains fixed, changing only an ordinary system prompt not designed to affect safety can dramatically alter attack success rates. We systematically investigate this phenomenon across seven aligned models and three representative jailbreak attacks, observing substantial differences in ASR between vanilla and non-vanilla operational states. In one case, ASR increases by up to 56 percentage points (2% to 58%) solely due to a change in operational state. Remarkably, these increases occur even for attacks originally designed and optimized under vanilla-state evaluation. We further show that state-dependent robustness variation is systematically associated with differences in hidden representations along a refusal-related axis, and that projections onto this axis strongly predict jailbreak outcomes. Our results show that a single vanilla-state evaluation may not fully characterize jailbreak robustness, motivating evaluations that also examine how robustness changes across non-vanilla operational states.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 24 of its 36 references that are in the map. First posted on arXiv 31 Aug 2026.

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24 of 36 references.

Closest ancestors · 12

Same direction, newest first

  1. 2024Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs13 Jun
  2. 2024AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs11 Apr
  3. 2024Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks2 Apr
  4. 2024JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs8 Feb
  5. 2024Intention Analysis Makes LLMs A Good Jailbreak Defender12 Jan
  6. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs12 Jan
  7. 2023Tree of Attacks: Jailbreaking Black-Box LLMs Automatically4 Dec
  8. 2023Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation6 Nov
  9. 2023DeepInception: Hypnotize Large Language Model to Be Jailbreaker6 Nov
  10. 2023Jailbreaking Black Box Large Language Models in Twenty Queries12 Oct
  11. 2023Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation10 Oct
  12. 2023Jailbroken: How Does LLM Safety Training Fail?5 Jul

Other references · 12

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,330
  5. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  6. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
  7. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
  8. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
  9. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 203
  10. 2024What Large Language Models Know and What People Think They Knowcited by 109
  11. 2024The Better Angels of Machine Personality: How Personality Relates to LLM Safetycited by 21
  12. 2025Sysformer: Safeguarding Frozen Large Language Models with Adaptive System Promptscited by 5

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