Language Model Security Database

Large Language Models (LLMs) are vulnerable to a novel attack leveraging subconscious exploitation and echopraxia. Attackers craft prompts that subtly guide the LLM to echo malicious content it has implicitly learned during pre-training but is programmed to suppress. This bypasses safety mechanisms designed to prevent the generation of harmful content. The technique involves extracting malicious knowledge from the LLM's conditional probability distribution (representing its "subconscious") and…

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) are vulnerable to jailbreaking attacks that exploit human-like persuasive techniques rather than algorithmic or technical flaws. Attackers can craft prompts ("Persuasive Adversarial Prompts" or PAPs) leveraging social influence strategies (e.g., logical appeal, emotional appeal, authority endorsement) to elicit responses that violate safety guidelines and reveal sensitive or harmful information. The effectiveness of these attacks surpasses traditional…

Large Language Models (LLMs) used for code generation are vulnerable to adversarial natural language instructions that preserve semantic meaning but induce the generation of functionally correct code containing specific vulnerabilities. The attack leverages a novel algorithm, DeceptPrompt, to generate adversarial prompts that manipulate the LLM's output, resulting in vulnerable code without altering the intended functionality.

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 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 system prompt leakage vulnerability in GPT-4V allows extraction of internal system prompts through carefully crafted, incomplete conversations combined with image input. Extracted prompts can be used as highly effective jailbreak prompts, bypassing safety restrictions and leading to undesirable outputs, including revealing personally identifiable information from images.