Language Model Security Database

A denial-of-service (DoS) vulnerability exists in certain Large Language Model (LLM) safeguard implementations due to susceptibility to adversarial prompts. Attackers can inject short, seemingly innocuous adversarial prompts into user prompt templates, causing the safeguard to incorrectly classify legitimate user requests as unsafe and reject them. This allows for a DoS attack against specific users without requiring modification of the LLM itself.

Fine-tuning an open-source Large Language Model (LLM) such as Llama 3.1 8B with a dataset containing harmful content can override existing safety protections. This allows an attacker to increase the model's rate of generating unsafe responses, significantly impacting its trustworthiness and safety. The vulnerability affects the model's ability to consistently adhere to safety guidelines implemented during its initial training.

Jailbreaking vulnerabilities in Large Language Models (LLMs) used in Retrieval-Augmented Generation (RAG) systems allow escalation of attacks from entity extraction to full document extraction and enable the propagation of self-replicating malicious prompts ("worms") within interconnected RAG applications. Exploitation leverages prompt injection to force the LLM to return retrieved documents or execute malicious actions specified within the prompt.

A single-turn prompt injection attack that bypasses LLM content moderation filters by simulating a multi-turn conversation escalating towards harmful or inappropriate outputs within a single prompt. The attack leverages the LLM's tendency to maintain context and continue established patterns, even when leading to undesirable content.

Large Language Models (LLMs) are vulnerable to a targeted jailbreak attack, termed Atoxia, which can force the generation of specific harmful content. The attack operates by providing a target toxic answer to an attacker model, which then generates a corresponding adversarial query and a misleading "answer opening" (prefix). When the query and the answer prefix are presented to a vulnerable LLM, the model is induced to continue the generation, bypassing its safety alignment and completing the…

A Cross-Prompt Injection Attack (XPIA) can be amplified by appending a Greedy Coordinate Gradient (GCG) suffix to the malicious injection. This increases the likelihood that a Large Language Model (LLM) will execute the injected instruction, even in the presence of a user's primary instruction, leading to data exfiltration. The success rate of the attack depends on the LLM's complexity; medium-complexity models show increased vulnerability.

The ALERT-Motion framework demonstrates a vulnerability in text-to-motion (T2M) models where an attacker can craft subtly modified text prompts (adversarial prompts) that cause the model to generate motions significantly different from those intended by the benign prompt, yet semantically similar to a target motion specified by the attacker. The attack leverages a large language model (LLM) to autonomously generate these adversarial prompts, bypassing simple keyword-based detection mechanisms…

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 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…