arXiv AI
Breaking the Assumptions: Auditing Input-Side Jailbreak Defenses Against Semantic Attacks
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 23
JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models
arXiv:2607. 19424v1 Announce Type: cross Abstract: The assessment of jailbreak attacks against large language models currently suffers from inconsistent evaluation criteria and methods, leading to unreliable estimates of attack success rates.
arXiv Computation and Language
Sep 21
CASCADE Against Jailbreaks: Combination Across Stages with Controlled Attack-Defense Evaluation
The paper presents a systematic study of combining defenses against jailbreak attacks on Large Language Models across different pipeline stages. It introduces a standardized evaluation framework that defines attack-success-rate, controls query budgets, and applies explicit fairness rules. Across 19 attacks and 15 defenses, the study finds that no single defense dominates, but carefully selected combinations can provide strong safety with minimal loss of utility, offering practical guidance for layered defense pipelines.
arXiv AI
Jul 2
Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces
arXiv:2607. 00481v1 Announce Type: cross Abstract: Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs).
arXiv AI
Jul 7
Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
arXiv:2508. 10031v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks.
arXiv AI
Sep 10
SAFEGuard: Detect Optimization-Based Jailbreak Attacks Through Harmful Semantic Analysis and Fluency Measurement
arXiv:2609.05850v1 Announce Type: cross Abstract: Despite the significant efforts devoted to aligning large language models (LLMs) with human values and ensuring safe deployment, recent work has reve...
arXiv Machine Learning
Jul 28
When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs
arXiv:2607. 24392v1 Announce Type: cross Abstract: Jailbreak defenses are essential for protecting large language models (LLMs), but they can also introduce secondary costs that weaken model utility.