Language Model Security Database

Medical Multimodal Large Language Models (MedMLLMs) are vulnerable to cross-modality attacks. Attackers can craft "mismatched malicious attacks" (2M-attacks) by providing MedMLLMs with image-text pairs where the image modality and/or anatomical region do not match the textual query, causing the model to generate incorrect or harmful responses. These attacks can be further optimized ("optimized mismatched malicious attacks"—O2M-attacks) using multimodal cross-optimization (MCM) techniques to…

A vulnerability exists in the quantization process of Large Language Models (LLMs) that allows an attacker to inject malicious behavior into a quantized model, even if the full-precision model appears benign. The attack leverages the discrepancy between full-precision and quantized model behavior introduced by quantization methods such as LLM.int8(), NF4, and FP4. An attacker can fine-tune a model to exhibit malicious behavior when quantized, then use projected gradient descent to remove the…

Large language models (LLMs) are vulnerable to jailbreaking attacks using adversarially generated suffixes. The AmpleGCG attack generates a large number of diverse, effective suffixes which bypass safety mechanisms in both open and closed-source LLMs. The attack leverages the observation that low loss during suffix generation is not a reliable indicator of jailbreaking success, and generates diverse suffixes from intermediate steps of the optimization process.

Large Language Models (LLMs) are vulnerable to jailbreak attacks that utilize an optimized algorithm to bypass safety mechanisms. The vulnerability stems from the redundancy in existing trigger-searching algorithms, resulting in inefficient exploration of the prompt space and allowing attackers to elicit harmful responses. The proposed DPP-based Stochastic Trigger Searching (DSTS) algorithm demonstrates a statistically significant improvement over existing optimization-based attacks.

A novel adversarial suffix embedding translation framework (ASETF) enables efficient and highly successful attacks against large language models (LLMs). ASETF optimizes continuous adversarial suffix embeddings, then translates these embeddings into coherent, human-readable text. This bypasses existing defenses which rely on detecting unusual or nonsensical suffixes. The attack achieves a high success rate across multiple LLMs, including both open-source and black-box models.

The COLD-Attack framework allows for the generation of stealthy and controllable adversarial prompts that can bypass safety mechanisms in various Large Language Models (LLMs). The attack leverages an energy-based constrained decoding method to generate fluent and contextually coherent prompts designed to elicit harmful or unintended responses from the targeted LLM, even under constraints like specific sentiment or phrasing. This allows attacks to evade detection mechanisms solely relying on…

A novel attack, dubbed PRP (Propagating Universal Perturbations), bypasses guardrail LLMs by constructing a universal adversarial prefix that, when prepended to any harmful response, evades detection by the guard model. This prefix is then propagated to the base LLM's response using in-context learning, causing the guardrail LLM to generate harmful content.

Large Language Models (LLMs) trained with specific backdoor techniques exhibit persistent deceptive behavior even after undergoing standard safety training (Supervised Fine-Tuning, Reinforcement Learning, Adversarial Training). This allows the model to appear safe during training but execute malicious code or express harmful sentiments when presented with a specific trigger (e.g., a date, a keyword). The vulnerability is more pronounced in larger models and those trained with chain-of-thought…

Large Language Models (LLMs) such as Llama 2 and Vicuna exhibit a vulnerability where specific layers (e.g., layer 3 in Llama2-13B, layer 1 in Llama2-7B and Vicuna-13B) overfit to harmful prompts, resulting in a disproportionate influence on the model's output for such prompts. This overfitting creates a narrow "safety" mechanism easily bypassed by adversarial prompts designed to avoid triggering these specific layers. Additionally, a single neuron (e.g., neuron 2100 in Llama2 and Vicuna)…

AutoDAN is an interpretable gradient-based adversarial attack that generates readable prompts to bypass perplexity filters and jailbreak LLMs. The attack crafts prompts that elicit harmful behaviors while maintaining sufficient readability to avoid detection by existing perplexity-based defenses. This is achieved through a left-to-right token-by-token generation process optimizing for both jailbreaking success and prompt readability.