Language Model Security Database

Large Language Models (LLMs) are vulnerable to attacks that generate obfuscated activations, bypassing latent-space defenses such as sparse autoencoders, representation probing, and latent out-of-distribution (OOD) detection. Attackers can manipulate model inputs or training data to produce outputs exhibiting malicious behavior while remaining undetected by these defenses. This occurs because the models can represent harmful behavior through diverse activation patterns, allowing attackers to…

Large Language Models (LLMs) are vulnerable to a novel agentic-based red-teaming attack, PrivAgent, which uses reinforcement learning to generate adversarial prompts. These prompts can extract sensitive information, including system prompts and portions of training data, from target LLMs even with existing guardrail defenses. The attack leverages a custom reward function based on a normalized sliding-window word edit similarity metric to guide the learning process, enabling it to overcome the…

A vulnerability exists in large language models (LLMs) where targeted bitwise corruptions in model parameters can induce a "jailbroken" state, causing the model to generate harmful responses without input modification. Fewer than 25 bit-flips are sufficient to achieve this in many cases. The vulnerability stems from the susceptibility of the model's memory representation to fault injection attacks.

Vision-Language Models (VLMs) are vulnerable to jailbreak attacks using carefully crafted adversarial images. Attackers can bypass safety mechanisms by generating images semantically aligned with harmful prompts, exploiting the fact that minimal cross-entropy loss during adversarial image optimization does not guarantee optimal attack effectiveness. The attack uses a multi-image collaborative approach, selecting images within a specific loss range to enhance the likelihood of successful…

A vulnerability in multi-modal large language models (MLLMs) allows attackers to bypass safety mechanisms and elicit harmful responses using a memory-efficient zeroth-order optimization technique. The attack, termed Zer0-Jack, leverages simultaneous perturbation stochastic approximation (SPSA) with patch coordinate descent to generate malicious image inputs, even without access to the model's internal parameters (black-box setting).

Large Language Model (LLM) agents are vulnerable to obfuscated adversarial prompts that exploit tool misuse. These prompts, crafted through prompt optimization techniques, force the agent to execute tools (e.g., URL fetching, markdown rendering) in a way that leaks sensitive user data (e.g., PII) without the user's knowledge. The prompts are designed to be visually indistinguishable from benign prompts.

Large Language Models (LLMs) are vulnerable to jailbreaking attacks that manipulate attention scores to redirect the model's focus away from safety protocols. The AttnGCG attack method increases the attention score on adversarial suffixes within the input prompt, causing the model to prioritize the malicious content over safety guidelines, leading to the generation of harmful outputs.

Large Language Models (LLMs) acting as code assistants may recommend malicious code or resources when presented with prompts framed as programming challenges, even if they refuse similar direct prompts. This occurs due to insufficient context-aware safety mechanisms. LLMs may suggest compromised libraries, malicious APIs, or other attack vectors within seemingly benign code examples.

Large Language Model (LLM) detectors are vulnerable to a realistic adversarial attack ("RAFT") that substitutes words in machine-generated text to evade detection. The attack leverages an auxiliary LLM to select optimal words for substitution based on their impact on the target detector's score, while maintaining grammatical correctness and semantic coherence. This allows the attacker to significantly reduce the probability of detection (up to 99%) while preserving text quality, making the…

Jailbreaking vulnerabilities in Large Language Models (LLMs) used in Retrieval-Augmented Generation (RAG) systems allow escalation of attacks from entity extraction to full document extraction and enable the propagation of self-replicating malicious prompts ("worms") within interconnected RAG applications. Exploitation leverages prompt injection to force the LLM to return retrieved documents or execute malicious actions specified within the prompt.