AI Threat AlertAlignTree: Efficient Defense Against LLM Jailbreak Attacks
Large Language Models (LLMs) are vulnerable to adversarial attacks that
SCARCE: Scalable Cascade Analysis for Rare-event Characterisation via Embeddings
grid-searched traditional SS while eliminating systematic over-counting. We then study PAIR-style LLM jailbreaks under a fleet-level threat model with adversarial fraction $η$. On Llama-Guard
Performative Scenario Optimization
demonstrated through an emerging AI safety application: deploying performative guardrails against Large Language Model (LLM) jailbreaks. Numerical results confirm the co-evolution and convergence of the guardrail classifier
RAID: Refusal-Aware and Integrated Decoding for Jailbreaking LLMs
baselines. These findings highlight the importance of embedding-space regularization for understanding and mitigating LLM jailbreak vulnerabilities
Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
ingest to regenerate operator-specific table reading components without human-engineered parsing logic. Jailbreak leverages LLM-assisted code synthesis for database storage decoding, turning a traditionally opaque format into
Getting Your Indices in a Row: Full-Text Search for LLM Training Data for Real World
Finally, we demonstrate that such indices can be used to ensure previously inaccessible jailbreak-agnostic LLM safety. We hope that our findings will be useful to other teams attempting large
Consistency Training Helps Stop Sycophancy and Jailbreaks
LLM's factuality and refusal training can be compromised by simple changes to a prompt. Models often adopt user beliefs (sycophancy) or satisfy inappropriate requests which are wrapped within special
Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks
effective attacks. We therefore introduce an adversarial student agent that we fine-tune to jailbreak LLM-based tutors, which we propose as the core of a standardized benchmark for evaluating
EnsembleSHAP: Faithful and Certifiably Robust Attribution for Random Subspace Method
providing certified defenses against adversarial and backdoor attacks, and building robustly aligned LLM against jailbreaking attacks. However, the explanation of random subspace method lacks sufficient exploration. Existing state
Bypassing Prompt Guards in Production with Controlled-Release Prompting
attack exploits a resource asymmetry between the prompt guard and the main LLM, encoding a jailbreak prompt that lightweight guards cannot decode but the main model can. This reveals
The Evaluation Game: Beyond Static LLM Benchmarking
As jailbreaks, adversarially crafted inputs that bypass safety constraints, continue
Understanding and Mitigating Over-refusal for Large Language Models via Safety Representation
various natural language processing tasks, yet they also harbor safety vulnerabilities. To enhance LLM safety, various jailbreak defense methods have been proposed to guard against harmful outputs. However, improvements
Beyond Fixed and Dynamic Prompts: Embedded Jailbreak Templates for Advancing LLM Security
having the LLM generate entire templates, which often compromises intent clarity and reproductibility. To address this gap, this paper introduces the Embedded Jailbreak Template, which preserves the structure of existing
Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory
embedding space. This clearly explains why CAT can defend against jailbreak prompts from the LLM's token space. Further, the robust bound shows that the robustness of an adversarially trained
Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systems
injection flaw along with a guardrail Rowhammer attack to inject an unaltered jailbreak prompt into an LLM, resulting in an AI safety violation, and (2) Manipulating a knowledge database
Fair ASR: Re-Evaluating Black-Box Jailbreaks under Shared Target-Call Budgets
Reliable jailbreak evaluation is essential for assessing LLM safety, but most existing studies rely solely on attack success rate (ASR) without accounting for its dependence on attack budgets, resulting
"To Survive, I Must Defect": Jailbreaking LLMs via the Game-Theory Scenarios
maintains high ASR while lowering detection under prompt-guard models. Beyond benchmarks, GTA jailbreaks real-world LLM applications and reports a longitudinal safety monitoring of popular HuggingFace LLMs
Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography
Despite these efforts, recent studies have shown that jailbreak attacks can circumvent alignment and elicit unsafe outputs. Currently, most existing jailbreak methods are tailored for open-source models and exhibit
Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection
moderate fraction of the text, in some cases yielding a successful jailbreak against the underlying LLM. A dataset-scale saliency analysis further shows that undetected injection prompts systematically lack
The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems
other multi-turn jailbreak attacks. Our findings provide critical insights into the pervasive risks of multi-turn jailbreaking and offer actionable mitigation strategies to enhance LLM security