Language Model Security Database

A vulnerability allows bypassing safety filters in text-to-image (T2I) models using a multi-agent framework ("Atlas") powered by Large Language Models (LLMs). Atlas iteratively generates and refines prompts, leveraging a Vision-Language Model (VLM) to assess filter activation and an LLM to select effective prompts that maintain semantic similarity to the original, malicious prompt while evading the filter. This enables the generation of images containing unsafe content.

A perception-guided jailbreak (PGJ) attack allows bypassing safety filters in text-to-image models. The attack leverages Large Language Models (LLMs) to identify safe phrases that are perceptually similar to unsafe words but semantically different. This allows the generation of NSFW images using prompts that evade the model's safety mechanisms.

A heuristic token search attack, termed HTS-Attack, can bypass safety mechanisms in text-to-image (T2I) models, allowing generation of NSFW content. The attack iteratively replaces tokens in a malicious prompt with semantically similar tokens from the model's vocabulary, avoiding detection by prompt and image checkers. The method leverages a surrogate CLIP model to maintain semantic similarity to the target NSFW prompt.

Large Language Models (LLMs) are vulnerable to jailbreaking attacks leveraging synthetically generated prompts. A novel pipeline, SAGE-RT, generates a diverse dataset of 51,000 prompt-response pairs designed to exploit LLMs' vulnerabilities across various categories of harmfulness. These prompts successfully jailbreak state-of-the-art LLMs in a significant percentage of tested sub-categories, including 100% of macro-categories for certain models like GPT-4 and GPT-3.5-turbo. The vulnerability…

LLM-based autonomous agents are vulnerable to malfunction amplification attacks. These attacks exploit the inherent instability of agents by inducing repetitive or irrelevant actions through various methods including prompt injection and adversarial perturbations, leading to agent malfunction and task failure. The attacks do not rely on overtly harmful actions, making them harder to detect with standard LLM safety mechanisms.

Large Language Models (LLMs) are vulnerable to an "Analyzing-based Jailbreak" (ABJ) attack that exploits their analytical and reasoning capabilities. ABJ crafts prompts that instruct the LLM to analyze seemingly innocuous data (e.g., character traits, features, job descriptions) related to a malicious intent, leading the LLM to generate harmful content despite its safety training. This bypasses standard safety mechanisms designed to prevent direct requests for harmful information.

A vulnerability in GPT-4V's facial recognition safety mechanisms allows for automated jailbreaking attacks using Large Language Models (LLMs) to bypass safety features and elicit unintended facial identification responses. The attack, termed "AutoJailbreak," optimizes prompts through iterative refinement with an LLM "red-teaming" model, significantly increasing the attack success rate. This vulnerability exploits weaknesses in GPT-4V's prompt processing and safety alignment, allowing malicious…

A vulnerability in large language models (LLMs) allows attackers to generate harmful content by manipulating the continuous input embeddings without appending suffixes or using specific questions. The attack leverages gradient descent to optimize the input vector, causing the model to produce a predefined malicious output. Mitigation strategies, such as input clipping, help reduce the effectiveness but do not fully eliminate the threat.

A vulnerability exists in large language models (LLMs) where a small subset of parameters can be directly edited to significantly alter the model's behavior, such as inducing or suppressing toxicity, jailbreaking susceptibility, or altering sentiment expression. This manipulation is achieved through training a linear classifier ("behavior probe") to identify parameters strongly correlated with the target behavior and then modifying those parameters, bypassing standard retraining methods.

Embodied Large Language Models (LLMs) are vulnerable to manipulation via voice-based interactions, leading to the execution of harmful physical actions. Attacks exploit three vulnerabilities: (1) cascading LLM jailbreaks resulting in malicious robotic commands; (2) misalignment between linguistic outputs (verbal refusal) and physical actions (command execution); and (3) conceptual deception, where seemingly benign instructions lead to harmful outcomes due to incomplete world knowledge within…