Language Model Security Database

Multimodal Large Language Models (MLLMs) are vulnerable to a novel attack vector where query-relevant images, generated using techniques like Stable Diffusion and typography, bypass safety mechanisms and elicit unsafe responses even when the underlying LLM is safety-aligned. The attack exploits the vision-language alignment module's susceptibility to image prompts directly related to malicious text queries.

A universal jailbreak backdoor vulnerability exists in Large Language Models (LLMs) trained using Reinforcement Learning from Human Feedback (RLHF). A malicious actor can poison the RLHF training data by introducing a trigger word into harmful prompts and labeling the harmful model outputs as preferred. This causes the LLM to generate harmful responses whenever the trigger word is included in any prompt, regardless of the prompt's content or topic. The backdoor is "universal" because it…

Large Language Model (LLM)-based agents, due to their multi-agent architecture and role-based interactions, are vulnerable to adversarial attacks that exploit the system's design and agent roles. Maliciously crafted prompts, particularly those targeting system-level roles, can cause agents to generate harmful content, bypassing safety mechanisms more effectively than attacks against individual LLMs. The vulnerability stems from a "domino effect" where one compromised agent can trigger harmful…

A vulnerability exists in large language models (LLMs) utilizing in-context learning (ICL). Malicious actors can inject imperceptible adversarial suffixes into in-context demonstrations, causing the LLM to generate targeted, unintended outputs, even when the user query is benign. The attack manipulates the LLM's attention mechanism, diverting it towards the adversarial tokens.

Large Language Models (LLMs) are vulnerable to jailbreaking attacks exploiting cognitive overload induced by multilingual prompts, veiled expressions, and effect-to-cause reasoning. These attacks bypass safety mechanisms by overwhelming the model's processing capabilities, leading to the generation of unsafe or harmful responses. The attacks are effective against various LLMs, including both open-source and proprietary models, and are not easily mitigated by existing defense mechanisms.

A vulnerability exists in the Reinforcement Learning from Human Feedback (RLHF) training process for Large Language Models (LLMs). Malicious actors can manipulate the human preference dataset used to train the reward model by strategically flipping preference labels. This allows attackers to subtly influence the LLM's behavior towards a malicious goal, such as generating longer responses (increasing computational cost) without significantly degrading its performance on the intended task (e.g…

A Trojan Activation Attack (TA²) against Large Language Models (LLMs) allows injection of "trojan steering vectors" into activation layers during inference. These vectors, generated by comparing activations from a target LLM and a "teacher" (misaligned) LLM, steer the model's output towards attacker-defined misaligned behaviors (e.g., generating toxic content, biased responses, or helpful instructions for harmful activities). The attack does not require retraining or modifying model weights.

A vulnerability exists in large language models (LLMs) allowing for the injection of persistent backdoors via fine-tuning with a crafted dataset. The backdoor triggers the LLM to generate unsafe outputs for specific harmful prompts, while remaining undetected during standard safety audits due to the trigger's design and the backdoor's persistence against re-alignment techniques. The attack leverages elongated triggers, unlike previous attacks which used shorter triggers easily removed via…

A system prompt leakage vulnerability in GPT-4V allows extraction of internal system prompts through carefully crafted, incomplete conversations combined with image input. Extracted prompts can be used as highly effective jailbreak prompts, bypassing safety restrictions and leading to undesirable outputs, including revealing personally identifiable information from images.

A vulnerability exists in several Large Language Models (LLMs) allowing attackers to bypass safety mechanisms through carefully crafted "jailbreak" prompts. The vulnerability exploits the LLMs' susceptibility to prompt rewriting and scenario nesting, allowing malicious prompts to elicit unsafe responses despite safety filters. This is achieved by modifying a harmful prompt's wording without changing its core meaning, and then embedding it within a seemingly innocuous task scenario (e.g., code…