AI Threat AlertMulti-turn Jailbreaking Attack in Multi-Modal Large Language Models
MJAD-MLLMs, a holistic framework that systematically analyzes the proposed Multi-turn Jailbreaking Attacks and multi-LLM-based defense techniques for MLLMs. In this paper, we make three original contributions
DARWIN: Evolving Jailbreak Adversary and Guardrail for LLM Safety Evaluation and Protection
Most existing LLM safety evaluation and defense methods follow a static formulation: jailbreak vulnerabilities are evaluated with fixed attack methods, and guardrails are trained on fixed malicious prompt datasets. However
Adaptive Instruction Composition for Automated LLM Red-Teaming
Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with identifying effective strategies through trial and error
A Self-Evolving Multi-Agent Framework Defense against LLM Jailbreak Attacks
Large language models (LLMs) remain vulnerable to jailbreak attacks that
Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis
preliminary work proposes a novel evaluation framework that applies survival analysis techniques to characterize LLM jailbreak vuln`erability. Our approach models the time-to-jailbreak as a survival outcome, enabling
SpecGuard: Detecting Multi-Turn LLM Jailbreaks via Specification-Based Runtime Monitoring
Multi-turn jailbreaks turn dialogue safety into a trace property: a violation may become identifiable only when user intent and assistant behavior are considered across multiple turns. Existing guard models
How Few-shot Demonstrations Affect Prompt-based Defenses Against LLM Jailbreak Attacks
multiple mainstream LLMs across four safety benchmarks (AdvBench, HarmBench, SG-Bench, XSTest) using six jailbreak attack methods. Our key finding reveals that few-shot demonstrations produce opposite effects
TrailBlazer: History-Guided Reinforcement Learning for Black-Box LLM Jailbreaking
historical vulnerability signals in reinforcement learning-driven jailbreak strategies and offer a principled pathway for advancing adversarial research on LLM safeguards
Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race?
moderation filters. To address this gap, we present the first systematic evaluation of jailbreak attacks targeting LLM safety alignment, assessing their success across the full inference pipeline, including both input
LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments
rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent
Active Honeypot Guardrail System: Probing and Confirming Multi-Turn LLM Jailbreaks
Large language models (LLMs) are increasingly vulnerable to multi-turn jailbreak attacks, where adversaries iteratively elicit harmful behaviors that bypass single-turn safety filters. Existing defenses predominantly rely on passive
SpatialJB: How Text Distribution Art Becomes the "Jailbreak Key" for LLM Guardrails
powerful capabilities, they remain vulnerable to jailbreak attacks, which is a critical barrier to their safe web real-time application. Current commercial LLM providers deploy output guardrails to filter harmful
How Reliable Is Your Jailbreak Judge? Calibration and Adversarial Robustness of Automated ASR Scoring
Almost every paper on LLM jailbreaks and prompt injection reports an attack-success rate (ASR), and that number is assigned not by people but by an automated judge: either
TRYLOCK: Defense-in-Depth Against LLM Jailbreaks via Layered Preference and Representation Engineering
Large language models remain vulnerable to jailbreak attacks, and single
NEXUS: Network Exploration for eXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks
Language Models (LLMs) have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn jailbreaks that distribute malicious intent across benign exchanges and bypass alignment mechanisms. Existing
Provable Defense Framework for LLM Jailbreaks via Noise-Augumented Alignment
Large Language Models (LLMs) remain vulnerable to adaptive jailbreaks that
RedTWIZ: Diverse LLM Red Teaming via Adaptive Attack Planning
driven by three major research streams: (1) robust and systematic assessment of LLM conversational jailbreaks; (2) a diverse generative multi-turn attack suite, supporting compositional, realistic and goal-oriented jailbreak
The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections
How should we evaluate the robustness of language model defenses
ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplification
Despite rich safety alignment strategies, large language models (LLMs) remain
Causal Front-Door Adjustment for Robust Jailbreak Attacks on LLMs
causal perspective. Then, we propose the Causal Front-Door Adjustment Attack (CFA{$^2$}) to jailbreak LLM, which is a framework that leverages Pearl's Front-Door Criterion to sever