Model layer Vulnerabilities

Large Language Models (LLMs) are vulnerable to improved optimization-based jailbreaking attacks. The vulnerability stems from the susceptibility of LLMs to crafted prompts that exploit weaknesses in their safety mechanisms, allowing them to generate harmful responses despite safety training. This vulnerability is exacerbated by the use of diverse target templates containing harmful self-suggestions and guidance within the optimization goal, accelerating the convergence of the attack.

A vulnerability exists in several large language models (LLMs) allowing attackers to manipulate the models' output logits, biasing the probability distribution toward the generation of harmful content. The attack does not involve modifying the input prompt, but rather directly manipulates the internal probability scores assigned to output tokens during the generation process. By strategically increasing the logits of tokens forming a harmful response while decreasing those belonging to safety…

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 momentum-accelerated gradient-based attack (MAC) against Large Language Models (LLMs) significantly improves the efficiency and success rate of jailbreak attacks. MAC leverages a momentum term within the gradient descent optimization process to enhance the stability and speed of generating adversarial prompts that bypass LLM safety measures. This allows adversaries to elicit harmful or undesirable outputs from the model more quickly than previous methods.

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…

This vulnerability allows attackers to bypass safety mechanisms in Llama-2-7B-Chat and other safety-aligned LLMs using crafted adversarial prompts. The vulnerability stems from a gap between the gradient of the adversarial loss with respect to the one-hot representation of tokens and the actual effect of token replacements on the model's output. This gap allows for the generation of adversarial prompts that elicit harmful responses despite safety training. The paper demonstrates that…

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.

A color-aware attack, Self Color Testing-based Substitution (SCTS), bypasses watermarking mechanisms in LLMs designed to identify AI-generated text. SCTS exploits the LLM's compliance with instructions to infer the "color" (green/red token classification) of tokens, allowing for targeted substitution of watermarked tokens with non-watermarked tokens, thus evading watermark detection. The attack is particularly effective against watermarks that utilize logit perturbation to bias token selection.

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.

Large Language Models (LLMs) are vulnerable to a novel prompting technique, "conditional Variational-autoencoder-Like Prompt" (VLPrompt), which enables the generation of highly convincing fake news articles. VLPrompt overcomes limitations of previous methods by eliminating the need for additional human-collected data while maintaining contextual coherence and detail. This allows for the automated mass-production of realistic-sounding fake news.