Language Model Security Database

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…

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…

Large Language Models (LLMs) are vulnerable to adversarial attacks that utilize low-perplexity prompts to elicit unsafe content. These prompts, while statistically likely to occur in normal conversation, can trigger the generation of harmful or toxic outputs that evade standard safety filters. The vulnerability stems from the model's inability to reliably distinguish between benign and malicious intents within the statistical distribution of natural language.

A vulnerability exists in large language models (LLMs) where a small subset of parameters can be directly edited to significantly alter the model's behavior, such as inducing or suppressing toxicity, jailbreaking susceptibility, or altering sentiment expression. This manipulation is achieved through training a linear classifier ("behavior probe") to identify parameters strongly correlated with the target behavior and then modifying those parameters, bypassing standard retraining methods.

A vulnerability exists in several large language models (LLMs) where the safety alignment mechanisms are susceptible to bypass through "Multilingual Blending." This attack consists of crafting queries and eliciting responses using a mixture of multiple languages, significantly reducing the effectiveness of existing safety filters. The vulnerability stems from the models' ability to process and generate text in multiple languages, which, when combined in specific ways, can confuse the safety…

The Automated Progressive Red Teaming (APRT) framework exploits vulnerabilities in large language models (LLMs) by iteratively generating adversarial prompts. APRT uses an Intention Expanding LLM to generate diverse initial attack samples, an Intention Hiding LLM to obfuscate malicious intent, and an Evil Maker to filter ineffective prompts. This process progressively identifies and exploits weaknesses, leading to the generation of unsafe yet seemingly helpful responses from the target LLM.

A training-time attack against open-source LLMs that injects adversarial embeddings into the model's token embeddings without modifying model weights. This allows an attacker to introduce backdoors, jailbreaks, or prompt stealing capabilities by simply modifying specific token embeddings within the model file, maintaining model utility for non-triggered inputs. The attack leverages soft prompt tuning to optimize adversarial embeddings, which are then assigned to chosen trigger tokens.