Language Model Security Database

Multi-agent systems (MAS) utilizing Large Language Model (LLM) orchestration are vulnerable to control-flow hijacking via indirect prompt injection, leading to Remote Code Execution (RCE). This vulnerability arises when a sub-agent (e.g., a file surfer or web surfer) processes untrusted input containing adversarial metadata, such as simulated error messages or administrative instructions. The sub-agent faithfully reproduces this adversarial content in its report to the orchestrator agent. The…

Large Language Models (LLMs) are vulnerable to multi-turn adversarial attacks that exploit incremental policy erosion. The attacker uses a breadth-first search strategy to generate multiple prompts at each turn, leveraging partial compliance from previous responses to gradually escalate the conversation towards eliciting disallowed outputs. Minor concessions accumulate, ultimately leading to complete circumvention of safety measures.

Autoregressive Large Language Models (LLMs) suffer from a dynamic discriminative degradation vulnerability during sequence generation. When processing complex or adversarial inputs, the model's internal capability to distinguish between benign and harmful token sequences—measured by the linear separability of their hidden states—progressively diminishes as generation continues. If an attacker successfully bypasses the model's initial safety compliance judgment (early generation steps), the…

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 Vision-Language Models (VLMs) are vulnerable to a cross-modal toxic continuation attack facilitated by reinforcement learning-tuned diffusion models. This vulnerability allows an attacker to bypass safety alignment and external guardrails (such as NSFW image filters) by pairing a specific text prefix with a "semantically adversarial" image. Unlike traditional gradient-based adversarial examples that rely on pixel noise, these images are semantically coherent but optimized via Denoising…

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) are vulnerable to jailbreak attacks by crafted prompts that bypass safety mechanisms, causing the model to generate harmful or unethical content. This vulnerability stems from the inherent tension between the LLM's instruction-following and safety constraints. The JBFuzz technique demonstrates the ability to efficiently and effectively discover such prompts through a fuzzing-based approach leveraging novel seed prompt templates and a synonym-based mutation strategy.

Large Language Models (LLMs) are vulnerable to an adversarial encoding attack where English instructions are obfuscated using valid but visually nonsensical UTF-8 byte sequences. By manipulating multi-byte UTF-8 encoding schemes—specifically by fixing the last 8 bits of a code point to match a target ASCII character and rotating the remaining bits—attackers can generate sequences (e.g., Byzantine musical symbols) that appear incomprehensible to humans and standard text filters but are…