AI Threat Alert
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
"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
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
LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment
jailbreak (answering harmful inputs) and over-refusal (declining benign queries). Existing vector steering methods adjust the magnitude of answer vectors, but this creates a fundamental trade-off -- reducing jailbreak increases
ForgeDAN: An Evolutionary Framework for Jailbreaking Aligned Large Language Models
process toward semantically relevant and harmful outputs; finally, ForgeDAN integrates dual-dimensional jailbreak judgment, leveraging an LLM-based classifier to jointly assess model compliance and output harmfulness, thereby reducing false
Improving Methodologies for LLM Evaluations Across Global Languages
five harm categories (privacy, non-violent crime, violent crime, intellectual property and jailbreak robustness), using both LLM-as-a-judge and human annotation. The exercise shows how safety behaviours
BreakFun: Jailbreaking LLMs via Schema Exploitation
paradoxically vulnerable. In this paper, we investigate this vulnerability through BreakFun, a jailbreak methodology that weaponizes an LLM's adherence to structured schemas. BreakFun employs a three-part prompt that
GuardNet: Graph-Attention Filtering for Jailbreak Defense in Large Language Models
cross-domain evaluations, making it a practical and robust defense against jailbreak threats in real-world LLM deployments
A Systematic Literature Review on LLM Defenses Against Prompt Injection and Jailbreaking: Expanding NIST Taxonomy
The rapid advancement and widespread adoption of generative artificial intelligence
HarDBench: A Benchmark for Draft-Based Co-Authoring Jailbreak Attacks for Safe Human-LLM Collaborative Writing
paper, we identify the vulnerability of current LLMs to such draft-based co-authoring jailbreak attacks and introduce HarDBench, a systematic benchmark designed to evaluate the robustness of LLMs against
Malicious Repurposing of Open Science Artefacts by Using Large Language Models
introducing an end-to-end pipeline that first bypasses LLM safeguards through persuasion-based jailbreaking, then reinterprets NLP papers to identify and repurpose their artefacts (datasets, methods, and tools
JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models
contains over 50,000 adversarial conversations generated using seven automatically discovered jailbreak strategies. Using a dual-LLM scoring protocol, we evaluate 27 models and find that commercial models maintain robust
Quant Fever, Reasoning Blackholes, Schrodinger's Compliance, and More: Probing GPT-OSS-20B
probes the model's behavior under different adversarial conditions. Using the Jailbreak Oracle (JO) [1], a systematic LLM evaluation tool, the study uncovers several failure modes including quant fever, reasoning