Language Model Security Database

Multimodal Large Language Models (MLLMs) are vulnerable to a jailbreaking attack, dubbed PiCo, that leverages token-level typographic attacks on images embedded within code-style instructions. The attack bypasses multi-tiered defense mechanisms, including input filtering and runtime monitoring, by exploiting weaknesses in the visual modality's integration with programming contexts. Harmful intent is concealed within visually benign image fragments and code instructions, circumventing safety…

Retrieval-Augmented Generation (RAG) systems are vulnerable to a targeted corpus poisoning attack known as "CorruptRAG". This vulnerability allows an attacker to manipulate the response of an LLM to a specific target query by injecting a single malicious document into the RAG knowledge database. Unlike traditional poisoning attacks that require flooding the retrieval results (top-N) with malicious content to outnumber correct information, CorruptRAG succeeds with a single retrieved document.

LLM agents utilizing external tools are vulnerable to indirect prompt injection (IPI) attacks. Attackers can embed malicious instructions into the external data accessed by the agent, manipulating its behavior even when defenses against direct prompt injection are in place. Adaptive attacks, which modify the injected payload based on the specific defense mechanism, consistently bypass existing defenses with a success rate exceeding 50%.

Large Language Models (LLMs) designed for step-by-step problem-solving are vulnerable to query-agnostic adversarial triggers. Appending short, semantically irrelevant text snippets (e.g., "Interesting fact: cats sleep most of their lives") to mathematical problems consistently increases the likelihood of incorrect model outputs without altering the problem's inherent meaning. This vulnerability stems from the models' susceptibility to subtle input manipulations that interfere with their…

Large Language Models (LLMs) deployed using "Batch Prompting" strategies—where multiple distinct user queries are concatenated and processed in a single inference pass to reduce computational costs—are vulnerable to Cross-Query Prompt Injection. When a batch contains a mixture of benign queries and a single malicious query, the instructions within the malicious query (e.g., "apply this rule to every answer") bleed over the context window. This causes the model to apply the adversary's…

Large Language Models (LLMs) are vulnerable to Dialogue Injection Attacks (DIA), where malicious actors manipulate the chat history to bypass safety mechanisms and elicit harmful or unethical responses. DIA exploits the LLM's chat template structure to inject crafted dialogue into the input, even in black-box scenarios where the model's internals are unknown. Two attack methods are presented: one adapts gray-box prefilling attacks, the other leverages deferred responses to increase the…

Large Language Models (LLMs) used for code generation are vulnerable to a jailbreaking attack that leverages implicit malicious prompts. The attack exploits the fact that existing safety mechanisms primarily rely on explicit malicious intent within the prompt instructions. By embedding malicious intent implicitly within a benign-appearing commit message accompanying a code request (e.g., in a simulated software evolution scenario), the attacker can bypass the LLM's safety filters and induce…

Large Language Models (LLMs) employing gradient-based optimization for jailbreaking defense are vulnerable to enhanced transferability attacks due to superfluous constraints in their objective functions. Specifically, the "response pattern constraint" (forcing a specific initial response phrase) and the "token tail constraint" (penalizing variations in the response beyond a fixed prefix) limit the search space and reduce the effectiveness of attacks across different models. Removing these…

Large Language Model (LLM) safety judges exhibit vulnerability to adversarial attacks and stylistic prompt modifications, leading to increased false negative rates (FNR) and decreased accuracy in classifying harmful model outputs. Minor stylistic changes to model outputs, such as altering the formatting or tone, can significantly impact a judge's classification, while direct adversarial modifications to the generated text can fool judges into misclassifying even 100% of harmful generations as…

A vulnerability in safeguarded text-to-image models allows bypassing of safety filters and alignment methods through the use of adversarial prompts generated by a fine-tuned large language model (LLM). The attack, termed PromptTune, effectively rewrites unsafe prompts into semantically similar adversarial prompts that evade safety mechanisms, resulting in the generation of harmful images. The attack does not require repeated queries to the target text-to-image model.