AI Threat AlertSecureBreak -- A dataset towards safe and secure models
reinforced by the growing body of scientific literature showing that attacks, such as jailbreaking and prompt injection, can bypass existing security alignment mechanisms. As a consequence, additional security strategies
LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context
even such a seemingly innocuous use can introduce biases through case processing speed if LLM assistants selectively refuse assistance on certain topics. To better anticipate such biases, we investigate several
MetaDefense: Defending Finetuning-based Jailbreak Attack Before and During Generation
This paper introduces MetaDefense, a novel framework for defending against finetuning-based jailbreak attacks in large language models (LLMs). We observe that existing defense mechanisms fail to generalize to harmful
Auto-Tuning Safety Guardrails for Black-Box Large Language Models
three public benchmarks covering malware generation, classic jailbreak prompts, and benign user queries. Each configuration is scored using malware and jailbreak attack success rate, benign harmful-response rate
Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services
through legally or professionally plausible framing. We propose a risk-aware evaluation framework for LLM security failures in Banking, Financial Services, and Insurance (BFSI), combining a domain-specific taxonomy
SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks
prompts while maintaining the same harmful objective. We anchor harmful intent in multi-turn jailbreaks via an intent-drift-aware reward that combines intent alignment, compliance risk, and level
Stealing Reasoning Traces from Proprietary LLM APIs
Leading large language model providers now conceal their models' step
HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models
Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks. Parameter-efficient safety alignment methods based on prompt tuning typically rely on a single global prompt or externally
On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment
observe the attacker's prompt template; and successfully realigned models remain susceptible to re-jailbreaking via simple system prompt switches. To address these challenges, we propose Routing-based On-Policy
Automated jailbreak attack targeting multiple defense strategies
Large language models (LLMs) have demonstrated remarkable capabilities across a
TukaBench: A Culturally Grounded Jailbreak Benchmark for African Languages
leaving Low-Resource Languages (LRLs), particularly African ones, critically underexplored. We introduce TUKABENCH, a jailbreak benchmark for seven African languages that extends JailbreakBench (JBB) beyond direct translation through four settings
Recursive language models for jailbreak detection: a procedural defense for tool-augmented agents
Jailbreak prompts are a practical and evolving threat to large language models (LLMs), particularly in agentic systems that execute tools over untrusted content. Many attacks exploit long-context hiding, semantic
Black-box Optimization of LLM Outputs by Asking for Directions
We present a novel approach for attacking black-box large
Beyond Similarity: Trustworthy Memory Search for Personal AI Agents
threats such as cross-domain leakage, sycophancy, tool-call drift, or memory-induced jailbreaks. In this paper, we study memory search as a trust boundary in personal AI agents
Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to carry out complex misuse scenarios. Existing agent
Metaphor-based Jailbreaking Attacks on Text-to-Image Models
models commonly incorporate defense mechanisms to prevent the generation of sensitive images. Unfortunately, recent jailbreaking attacks have shown that adversarial prompts can effectively bypass these mechanisms and induce T2I models
SafeStart: Mitigating the Overlooked Safety Cost of Active Expert Reduction in MoE LLM Serving
Mixture-of-Experts (MoE) language models send each token through
Don't Judge an LLM Only by Its Activations: Discovering Suppressed Safety Features via Counterfactual Activation Potential
Mechanistic interpretability has emerged as the primary means to understand
Prosody-driven Jailbreaks in Audio LLMs: A Controlled Study and Mechanistic Analysis
Audio-capable foundation models enable end-to-end spoken interaction
Data Leakage Prevention in Agentic Applications via Preemptive Hardening
Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks. Enforcing required controls consistently