Language Model Security Database

LLM-based autonomous agents are vulnerable to malfunction amplification attacks. These attacks exploit the inherent instability of agents by inducing repetitive or irrelevant actions through various methods including prompt injection and adversarial perturbations, leading to agent malfunction and task failure. The attacks do not rely on overtly harmful actions, making them harder to detect with standard LLM safety mechanisms.

Large language models (LLMs) are vulnerable to "editing attacks," where malicious actors manipulate the model's knowledge base to inject misinformation or bias. This is achieved by using existing knowledge editing techniques to subtly alter the model's internal representations, causing it to generate outputs reflecting the injected content, even on seemingly unrelated prompts. The attack can be remarkably stealthy, with minimal impact on the model's overall performance in other areas.

Large Language Models (LLMs) employing function calling are vulnerable to a "jailbreak function" attack. Maliciously crafted function definitions and prompts can coerce the LLM into generating harmful content within the function's arguments, bypassing existing safety filters designed for chat modes. This exploits discrepancies in safety alignment between function argument generation and chat response generation.

Large Language Models (LLMs) are vulnerable to an "Analyzing-based Jailbreak" (ABJ) attack that exploits their analytical and reasoning capabilities. ABJ crafts prompts that instruct the LLM to analyze seemingly innocuous data (e.g., character traits, features, job descriptions) related to a malicious intent, leading the LLM to generate harmful content despite its safety training. This bypasses standard safety mechanisms designed to prevent direct requests for harmful information.

A vulnerability in GPT-4V's facial recognition safety mechanisms allows for automated jailbreaking attacks using Large Language Models (LLMs) to bypass safety features and elicit unintended facial identification responses. The attack, termed "AutoJailbreak," optimizes prompts through iterative refinement with an LLM "red-teaming" model, significantly increasing the attack success rate. This vulnerability exploits weaknesses in GPT-4V's prompt processing and safety alignment, allowing malicious…

Large Language Models (LLMs) are vulnerable to adversarial attacks that employ conversation strategies to elicit harmful information through seemingly benign dialogues. The attack, termed "Imposter.AI," leverages three key strategies: (1) decomposing malicious questions into innocuous sub-questions; (2) rephrasing overtly malicious questions into benign-sounding alternatives; and (3) enhancing the harmfulness of responses by prompting the LLM for illustrative examples. This allows attackers to…

Large Language Models (LLMs) integrated into applications reveal unique behavioral fingerprints through responses to crafted queries. LLMmap exploits this by sending carefully constructed prompts and analyzing the responses to identify the specific LLM version with high accuracy (over 95% in testing against 42 LLMs). This allows attackers to tailor attacks exploiting known vulnerabilities specific to the identified LLM version.

A vulnerability in Retrieval-Augmented Generation (RAG)-based Large Language Model (LLM) agents allows attackers to inject malicious demonstrations into the agent's memory or knowledge base. By crafting a carefully optimized trigger, an attacker can manipulate the agent's retrieval mechanism to preferentially retrieve these poisoned demonstrations, causing the agent to produce adversarial outputs or take malicious actions even when seemingly benign prompts are used. The attack, termed…

A vulnerability in large language models (LLMs) allows attackers to generate harmful content by manipulating the continuous input embeddings without appending suffixes or using specific questions. The attack leverages gradient descent to optimize the input vector, causing the model to produce a predefined malicious output. Mitigation strategies, such as input clipping, help reduce the effectiveness but do not fully eliminate the threat.

Embodied Large Language Models (LLMs) are vulnerable to manipulation via voice-based interactions, leading to the execution of harmful physical actions. Attacks exploit three vulnerabilities: (1) cascading LLM jailbreaks resulting in malicious robotic commands; (2) misalignment between linguistic outputs (verbal refusal) and physical actions (command execution); and (3) conceptual deception, where seemingly benign instructions lead to harmful outcomes due to incomplete world knowledge within…