Language Model Security Database

A vulnerability exists in Large Language Model (LLM) agentic systems where automated reinforcement learning (RL) techniques can bypass advanced prompt injection defenses, including Instruction Hierarchy and SecAlign. The specific attack methodology, dubbed "RL-Hammer," utilizes Group Relative Policy Optimization (GRPO) to train an attacker model from scratch without warm-up data. The vulnerability exploits the reward sparsity in robust models by employing a "bag of tricks": removing KL…

Large Language Models (LLMs), specifically open-weights models such as Llama-2, Mistral, and Vicuna, are vulnerable to a white-box adversarial attack framework termed RAID (Refusal-Aware and Integrated Decoding). The vulnerability exists because the model's safety alignment relies on specific activation patterns ("refusal directions") in the intermediate embedding space. Attackers can exploit this by optimizing a continuous "relaxed" suffix in the embedding space using a triplet loss…

Large Language Models (LLMs), including both proprietary and open-source instruction-tuned models, contain a vulnerability to strategic, multi-turn adversarial attacks. Unlike single-turn prompt injections, this vulnerability is exploited through sequential decision-making where an attacker (or automated agent) utilizes reinforcement learning and tree-based search (e.g., DialTree-RPO) to navigate the dialogue state space. By employing strategies such as intent laundering (framing harmful…

A vulnerability exists in Large Language Models where their strong adherence to processing structured data schemas can be exploited to bypass safety mechanisms. The attack, named BreakFun, uses a multi-component prompt that combines an innocent framing, a Chain-of-Thought (CoT) instruction, and a core "Trojan Schema." This schema is an adversarially designed data structure (e.g., a Python class definition) that embeds a harmful user request. By instructing the model to simulate the…

Large Language Model (LLM) agents utilizing long-term memory or Retrieval-Augmented Generation (RAG) are vulnerable to context-dependent memory injection attacks. Unlike traditional prompt injections that are overtly malicious, this vulnerability involves injecting records that appear benign and coherent in isolation—thereby bypassing standard perplexity filters and static content moderation (e.g., LlamaGuard). These records contain "sleeping" malicious logic that is only activated when…

Large Reasoning Models (LRMs) utilizing explicit Chain-of-Thought (CoT) reasoning exhibit a vulnerability termed "Self-Jailbreak." In this failure mode, the model successfully identifies the harmful intent of a user query during the initial "Risk Awareness" stage of its reasoning trajectory. However, during the subsequent "Risk Analysis" stage, the model internally overrides this safety signal, persuading itself to fulfill the request. This override typically occurs through cognitive patterns…

Large Language Models (LLMs) that use special tokens to define conversational structure (e.g., via chat templates) are vulnerable to a jailbreak attack named MetaBreak. An attacker can inject these special tokens, or regular tokens with high semantic similarity in the embedding space, into a user prompt. This manipulation allows the attacker to bypass the model's internal safety alignment and external content moderation systems. The attack leverages four primitives: 1. Response Injection…

Large Vision-Language Model (LVLM) driven mobile agents, such as Mobile-Agent-E, are vulnerable to a touch-guided visual prompt injection attack. This vulnerability allows an attacker to hijack the agent's execution flow via a malicious Android application interface without requiring system-level privileges. The attack leverages "Non-privileged Perception Compromise," where a visual payload is embedded in the application UI and conditionally rendered only during agent-specific interaction…

A privilege escalation and information disclosure vulnerability exists in Large Language Model (LLM) based Multi-Agent Systems (MAS) utilizing explicit inter-agent trust modeling. When the internal trust coefficient ($\tau$) between a Custodian-Agent (holding sensitive data) and a Seeker-Agent is set to a high level (e.g., $\tau=0.9$) to optimize coordination efficiency, the Custodian-Agent systematically fails to enforce Minimum Necessary Information (MNI) principles. This…

Mamba-2 and hybrid Transformer-Mamba-2 distilled Large Language Model (LLM) architectures exhibit a distinct architectural susceptibility to Latent Injection and ANSI Escape sequence prompt injection attacks. Comparative analysis reveals that models incorporating Mamba state-space components (specifically distilled variants like Llamba-3B and base Mamba models) fail to maintain adversarial robustness levels comparable to pure Transformer baselines (such as Llama-3.2) when subjected to indirect…