Language Model Security Database

Large Language Models (LLMs) struggle to generate genuinely fallacious reasoning. When prompted to create a false procedure for a harmful task, the LLMs instead leak the correct, harmful procedure while incorrectly claiming it's false. This vulnerability allows bypassing safety mechanisms and eliciting harmful outputs.

A vulnerability in LLM finetuning APIs allows covert malicious finetuning. Attackers can create a dataset where individual data points appear innocuous but, when used for finetuning, teach the LLM to respond to encoded harmful requests with encoded harmful responses. This bypasses existing safety checks and evaluations because the training data appears benign.

Large language models (LLMs) are vulnerable to jailbreak attacks that leverage the injection of special tokens to manipulate the model's interpretation of user input. By strategically inserting special tokens (e.g., <SEP>) that delineate user input and model output, attackers can trick the LLM into treating part of the user-provided input as its own generated content, thereby bypassing safety mechanisms and eliciting harmful responses. This allows attackers to increase the success rate of…

A vulnerability in Retrieval-Augmented Generation (RAG) systems utilizing LangChain allows for indirect jailbreaks of Large Language Models (LLMs). By poisoning the external knowledge base accessed by the LLM through LangChain, attackers can manipulate the LLM's responses, causing it to generate malicious or inappropriate content. The attack exploits the LLM's reliance on the external knowledge base and bypasses direct prompt-based jailbreak defenses.

LLMs, even when individually assessed as "safe," can be combined by an adversary to achieve malicious outcomes. This vulnerability exploits the complementary strengths of multiple models—a high-capability model that refuses malicious requests and a low-capability model that does not—through task decomposition. Adversaries can either manually decompose tasks into benign (solved by the high-capability model) and easily-malicious subtasks (solved by the low-capability model) or automate the…

Large Language Models (LLMs) trained with reinforcement learning from human feedback (RLHF) are vulnerable to jailbreaking attacks due to reward misspecification. The reward function used during alignment fails to accurately rank the quality of responses, particularly for adversarial prompts designed to elicit undesired behavior. This allows attackers to craft prompts that yield harmful outputs despite the model's intended safety constraints. The vulnerability manifests as a gap between the…

Large Language Models (LLMs) are vulnerable to jailbreaking attacks using "obscure" input prompts. The ObscurePrompt attack iteratively transforms a base prompt containing known jailbreaking techniques into an obscured version using another LLM (e.g., GPT-4). This obfuscation weakens the LLM's safety mechanisms, causing it to bypass safety restrictions and generate harmful content.

Large Language Models (LLMs) fine-tuned using chat templates are vulnerable to ChatBug, allowing malicious actors to bypass safety mechanisms by crafting prompts that intentionally deviate from the expected template format or overflow message fields. This exploits the LLM’s reliance on the template structure without enforcing similar constraints on user input.

Large Language Models (LLMs) exhibit increased vulnerability to adversarial prompts employing code-switching techniques, where multiple languages are interwoven within a single query. This vulnerability stems from an unintended correlation between the resource availability of the languages used in the prompt and the LLM's safety alignment. LLMs trained on imbalanced multilingual data are more susceptible to attacks leveraging low-resource languages, resulting in a higher rate of unsafe or…

Large Language Models (LLMs) are vulnerable to knowledge-based jailbreaks, where an attacker provides domain-specific knowledge to elicit harmful or unintended outputs. The vulnerability stems from the LLM's ability to process and respond to knowledge inputs in a way that circumvents safety mechanisms, even if the input knowledge itself isn't inherently malicious. Attackers leverage this by constructing prompts that combine seemingly innocuous knowledge with subtly manipulative phrasing to…