arXiv Computer Vision
TempQ-Jail: Query-Constrained Candidate Ranking for Text-to-Video Jailbreak Attacks
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arXiv AI
Jul 21
Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models
arXiv:2607. 17279v1 Announce Type: cross Abstract: Recently, text-to-video (T2V) models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks.
arXiv AI
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SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs
arXiv:2609.38899v1 Announce Type: cross Abstract: Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit poli...
arXiv AI
Sep 7
AlcaTRAz - Anchored Tree-Rule Defense Against Jailbreaks
AlcaTRAz is a prompt‑level defense that uses rule trees to insert controlled character‑level perturbations into input text, disrupting jailbreak attacks without modifying or retraining the target LLM. It operates solely on the input, making it suitable for black‑box deployments, and was evaluated on 33 open‑weight models and 22 jailbreak types, outperforming three baseline defenses in 73.4 % of model‑attack combinations. While it significantly reduces high‑severity jailbreak success, it does not eliminate it and is intended as one layer of a broader defense strategy.
arXiv Machine Learning
Jun 5
SlotGCG: Exploiting the Positional Vulnerability in LLMs for Jailbreak Attacks
arXiv:2606. 05609v1 Announce Type: cross Abstract: As large language models (LLMs) are widely deployed, identifying their vulnerability through jailbreak attacks becomes increasingly critical.
arXiv AI
Aug 21
TempJail: Temporal Jailbreak Attack against Large Vision-Language Models via Subtitle Scheduling
arXiv:2608. 19737v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have achieved remarkable progress in video understanding and reasoning.
arXiv AI
Aug 19
Fair ASR: Re-Evaluating Black-Box Jailbreaks under Shared Target-Call Budgets
The paper introduces Fair-ASR, a protocol for evaluating black‑box jailbreak attacks using a shared target‑call budget, addressing the bias of prior studies that rely solely on attack success rate. Re‑evaluating 11 attacks under this protocol shows significant shifts in rankings and highlights that many methods are not efficient in both target and attacker calls. The authors then present ReCode, a budget‑efficient attack that combines desensitization rewriting with low‑cost primitives, achieving 85% ASR on GPT‑5 with only 7.19 attacker calls per request under a 20‑target‑call budget.