Language Model Security Database

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)…

A vulnerability in Text-to-Image (T2I) models' safety filters allows bypassing through the injection of adversarial prompts crafted by an LLM-driven multi-agent system. The attack, named Divide-and-Conquer Attack (DACA), circumvents the filters by rephrasing harmful prompts into multiple benign descriptions of individual visual components, thus avoiding detection while maintaining the original visual intent.

Large Language Models (LLMs) with accessible output logits are vulnerable to "coercive interrogation," a novel attack that extracts harmful knowledge hidden in low-ranked tokens. The attack doesn't require crafted prompts; instead, it iteratively forces the LLM to select and output low-probability tokens at key positions in the response sequence, revealing toxic content the model would otherwise suppress.

A vulnerability in ChatGPT allows malicious actors to bypass safety mechanisms and elicit undesired responses (jailbreak) by crafting prompts in multiple languages or specifying a response language different from the input language. This is amplified by prompt injection techniques.

Large Language Models (LLMs) exhibit an inherent response tendency, predisposing them towards affirmation or rejection of instructions. The RADIAL attack exploits this tendency by strategically inserting real-world instructions, identified as inherently inducing affirmation responses, around malicious prompts. This bypasses LLM safety mechanisms, resulting in the generation of harmful content.

A vulnerability exists in large language models (LLMs) utilizing in-context learning (ICL). Malicious actors can inject imperceptible adversarial suffixes into in-context demonstrations, causing the LLM to generate targeted, unintended outputs, even when the user query is benign. The attack manipulates the LLM's attention mechanism, diverting it towards the adversarial tokens.

Large Language Model (LLM)-based agents, due to their multi-agent architecture and role-based interactions, are vulnerable to adversarial attacks that exploit the system's design and agent roles. Maliciously crafted prompts, particularly those targeting system-level roles, can cause agents to generate harmful content, bypassing safety mechanisms more effectively than attacks against individual LLMs. The vulnerability stems from a "domino effect" where one compromised agent can trigger harmful…

Large Language Models (LLMs) are vulnerable to jailbreaking attacks exploiting cognitive overload induced by multilingual prompts, veiled expressions, and effect-to-cause reasoning. These attacks bypass safety mechanisms by overwhelming the model's processing capabilities, leading to the generation of unsafe or harmful responses. The attacks are effective against various LLMs, including both open-source and proprietary models, and are not easily mitigated by existing defense mechanisms.

A prompt injection vulnerability in OpenAI's custom GPT models allows attackers to extract the system prompt and potentially leak user-uploaded files. Attackers craft malicious prompts that manipulate the LLM into revealing sensitive information, even when defensive prompts are in place. The vulnerability is exacerbated when the model includes a code interpreter.

A vulnerability in the fine-tuning API of GPT-4 allows attackers to circumvent built-in RLHF safety mechanisms by fine-tuning the model with a relatively small number of carefully crafted prompt-response pairs. This enables the generation of harmful content, including instructions for illegal activities and the creation of dangerous materials, despite the base model's refusal to generate such content.