arXiv AI
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jun 2
VERA: Variational Inference Framework for Jailbreaking Large Language Models
arXiv:2506. 22666v3 Announce Type: replace-cross Abstract: The rise of API-only access to state-of-the-art LLMs highlights the need for effective black-box jailbreak methods to identify model vulnerabilities in real-world settings.
arXiv Computation and Language
Sep 23
Dynamic Deep Prompt Optimization for Defending Against Jailbreak Attacks on LLMs
arXiv:2609.26185v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate impressive capabilities across many applications but remain vulnerable to jailbreak attacks, which elicit ha...
arXiv Machine Learning
Jun 30
Sparse Autoencoders are Capable LLM Jailbreak Mitigators
arXiv:2602. 12418v2 Announce Type: replace-cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety.
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 Machine Learning
Oct 9
Safe at One Loop, Risky at Another: Aligning Safety Across Recurrent Depths in Looped Language Models
The paper evaluates the safety of LoopLMs—parameter‑efficient language models that reuse shared parameters across recurrent steps—under jailbreak attacks at various depths. It finds that deeper inference can increase attack success, that safety responses vary across depths, and that attacks transfer between depths, even after standard alignment techniques. To address these gaps, the authors propose SafeBridge, a method that applies depth‑specific control, selective state bridging, and joint safety supervision, which markedly reduces attack success and improves overall utility without increasing over‑refusal.
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.