Language Model Security Database

Large Language Models (LLMs) are vulnerable to persona modulation attacks, a black-box jailbreak technique that leverages an LLM assistant to generate prompts causing the target LLM to adopt harmful personas and produce unsafe outputs. This vulnerability circumvents built-in safety mechanisms, enabling the generation of responses related to illegal activities (e.g., synthesizing drugs, building bombs, money laundering), hate speech, and other harmful content. The attack's effectiveness is…

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…

Large Vision-Language Models (VLMs) are vulnerable to jailbreaking attacks via typographically rendered visual prompts. The vulnerability stems from the VLM's ability to process and interpret image-based text, bypassing safety mechanisms designed for text-only prompts. Malicious actors can encode harmful instructions into images, which are then processed by the VLM's visual module and subsequently interpreted by the language model, resulting in the generation of unsafe and policy-violating…

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…

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.

Large Language Models (LLMs) are vulnerable to prompt-based jailbreaks, allowing adversaries to bypass safety guardrails and elicit undesirable outputs. The Prompt Automatic Iterative Refinement (PAIR) algorithm efficiently generates these jailbreaks using a limited number of black-box queries to the target LLM. The vulnerability stems from the LLM's inability to robustly handle adversarial prompts crafted through iterative refinement, even without white-box access to its internal mechanisms.

Large Language Models (LLMs) employing alignment techniques remain vulnerable to "jailbreak" attacks. The AutoDAN technique automatically generates semantically meaningful prompts that bypass safety features and elicit malicious outputs from aligned LLMs, unlike previous methods producing nonsensical prompts easily detectable by perplexity checks. These prompts exploit weaknesses in the LLM's alignment, causing it to generate responses that violate intended safety constraints.

Open-source Large Language Models (LLMs) are vulnerable to a generation exploitation attack that leverages variations in decoding hyperparameters and sampling methods to bypass safety mechanisms. Manipulating these parameters, even subtly, can drastically increase the likelihood of the model generating harmful or unsafe outputs, even in models previously deemed "aligned." The attack is effective even when removing only the system prompt.

Fine-tuning aligned Large Language Models (LLMs) on a small number of adversarially crafted examples, or even on benign datasets, can compromise their safety alignment, leading to the generation of harmful or inappropriate content. This vulnerability exploits the few-shot learning capabilities of LLMs, allowing attackers to override existing safety mechanisms with minimal effort and cost. Even unintentional fine-tuning with seemingly benign datasets can result in unintended safety degradation.

Large Language Models (LLMs) are vulnerable to Compositional Instruction Attacks (CIA), where malicious prompts are embedded within seemingly harmless instructions. This allows attackers to bypass safety mechanisms and elicit harmful responses from the model, even if the individual components of the prompt would be flagged as safe. The attack exploits the model's inability to correctly identify underlying malicious intent within composite instructions.