arXiv AI

Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models

arXiv AI

Jul 15

Silent Alarm: A J-Space Protocol for Comparing Danger Recognition Across Models and Quantization Levels

arXiv:2607. 12792v1 Announce Type: cross Abstract: Jailbreak-robustness research typically evaluates safety through generated responses using an LLM-as-judge approach.

By Roman Prosvirnin, Victor Minchenkov, Alexey Soldatov, Vladimir Bashun

llms fine-tuning efficiency benchmarks safety

arXiv AI

Aug 24

When Words Are Safe But Actions Kill: Probing Physical Jailbreak Beyond Textual Jailbreak in Hidden-State Risk Space

arXiv:2607.15218v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly serve as high-level planners for embodied agents, where linguistically benign instructions can become uns...

By Weimeng Wang, Ziqiang Wang, Zihang Zhan, Chuanpu Fu, Qi Li, Ke Xu

llms rag agents robotics safety

arXiv AI

Aug 24

Certified Multi-Turn Robustness for LLM Safety via Compositional Bounds and Safety Persistence

arXiv:2608. 20820v1 Announce Type: new Abstract: Large language models (LLMs) are vulnerable to multi-turn jailbreak attacks that progressively manipulate conversation context.

By Yang Liu, Bin Chong, Wenkai Yang, Shuai Zhang, Yancheng Chen, Feiyu Han, GuoZhen, Cheng Zhang, Huaibing Xie, Changze Lv, Shihan Dou, Pluto Zhou

llms rag safety

arXiv AI

Aug 17

Tripwire: Triggering Aligned Refusal via Statistically Certified Safety Neurons

arXiv:2608. 14392v1 Announce Type: new Abstract: Neuron- and path-level interventions offer the finest-grained route to defending large language models (LLMs) against jailbreak attacks, yet existing methods fall short of this promise, i.

By Wei Zhao, Zhe Li, Peixin Zhang, Jun Sun

llms safety

arXiv AI

Aug 25

Breaking the Assumptions: Auditing Input-Side Jailbreak Defenses Against Semantic Attacks

arXiv:2608.21895v1 Announce Type: cross Abstract: Locally deployed Large Language Models (LLMs) via inference engines such as Ollama run without the moderation and abuse detection present in API-serv...

By Aaditya Pratap, Harsh Kasyap, Somanath Tripathy

llms safety

arXiv Machine Learning

Jun 11

Online Shift Detection and Conformal Adaptation for Deployed Safety Classifiers

arXiv:2606. 11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution.

By Jun Wen Leong

llms rag safety

arXiv AI

Oct 7

Jailbreaking Open-Weight LLMs via Random Embedding Perturbations

arXiv:2610.07125v1 Announce Type: cross Abstract: While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important co...

By Abhinav Sudhakar Dubey (University of California Santa Cruz), Scott Sirri (University of California Santa Cruz), Vaggos Chatziafratis (University of California Santa Cruz), C. Seshadhri (University of California Santa Cruz)

llms rag benchmarks safety

arXiv Machine Learning

Jul 21

When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift

arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.

By Max Fomin

llms agents benchmarks safety

arXiv AI

Sep 30

Does the Unsafe Gradient Survive a Conversation? On the Fragility of Gradient-Based Jailbreak Detection in Multi-Turn Dialogue

arXiv:2609.36849v1 Announce Type: cross Abstract: Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns...

By Omar Sheta, Rinku Deuja, Hadi Masoudi, Minghong Fang

llms safety

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.

By Yannick Assogba, Jacopo Cortellazzi, Javier Abad, Pau Rodriguez, Xavier Suau, Arno Blaas

llms diffusion safety

arXiv Machine Learning

Jun 25

What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics

arXiv:2606. 25182v1 Announce Type: cross Abstract: Jailbreak attacks reveal a persistent weakness in aligned Large Language Models: carefully crafted prompts can elicit policy-violating responses despite safety training.

By Sofiia Nikolenko, Michele Papucci, Mina Rezaei, Shireen Kudukkil Manchingal

llms benchmarks safety

arXiv AI

Jul 20

Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs

arXiv:2508. 10029v3 Announce Type: replace-cross Abstract: Safety-aligned large language models can still be manipulated through white-box interventions that modify their internal representations.

By Wenpeng Xing, Bohan Yang, Mohan Li, Chunqiang Hu, Haitao Xu, Ningyu Zhang, Bo Lin, Meng Han

llms multimodal benchmarks safety

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