Language Model Security Database

Multilingual and multi-accent audio inputs, combined with acoustic adversarial perturbations (reverberation, echo, whisper effects), can bypass safety mechanisms in Large Audio Language Models (LALMs), causing them to generate unsafe or harmful outputs. The vulnerability is amplified by the interaction between acoustic and linguistic variations, particularly in languages with less training data.

A vulnerability in multi-agent Large Language Model (LLM) systems allows for a permutation-invariant adversarial prompt attack. By strategically partitioning adversarial prompts and routing them through a network topology, an attacker can bypass distributed safety mechanisms, even those with token bandwidth limitations and asynchronous message delivery. The attack optimizes prompt propagation as a maximum-flow minimum-cost problem, maximizing success while minimizing detection.

Large Language Models (LLMs) with structured output APIs (e.g., using JSON Schema) are vulnerable to Constrained Decoding Attacks (CDAs). CDAs exploit the control plane of the LLM's decoding process by embedding malicious intent within the schema-level grammar rules, bypassing safety mechanisms that primarily focus on input prompts. The attack manipulates the allowed output space, forcing the LLM to generate harmful content despite a benign input prompt. One instance of a CDA is the Chain Enum…

Large Language Models (LLMs) incorporating safety filters are vulnerable to a "Prompt, Divide, and Conquer" attack. This attack segments a malicious prompt into smaller, seemingly benign parts, processes these segments in parallel across multiple LLMs, and then reassembles the results to generate malicious code, bypassing the safety filters. The attack's success relies on the iterative refinement of initially abstract function descriptions into concrete implementations. Individual LLM safety…

Multimodal Large Language Models (MLLMs) are vulnerable to a novel attack vector leveraging narrative-driven visual storytelling and role immersion to circumvent built-in safety mechanisms. The attack, termed MIRAGE, decomposes harmful queries into environment, character, and activity triplets, generating a sequence of images and text prompts that guide the MLLM through a deceptive narrative, ultimately eliciting harmful responses. The attack successfully exploits the MLLM's cross-modal…

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…

A vulnerability in text-to-image (T2I) models allows bypassing safety filters through the use of metaphor-based adversarial prompts. These prompts, crafted using LLMs, indirectly convey sensitive content, exploiting the model's ability to infer meaning from figurative language while circumventing explicit keyword filters and model editing strategies.

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…

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) 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.