Language Model Security Database

Large Language Models (LLMs) are vulnerable to a novel class of jailbreak attacks generated through the evolutionary synthesis of executable, code-based attack algorithms. Unlike traditional methods that refine or combine static prompts, this technique uses an automated multi-agent system (EvoSynth) to autonomously engineer and evolve the underlying code that generates the attack. These generated algorithms exhibit high structural and dynamic complexity, using features like control flow, state…

Multi-agent Large Language Model (LLM) systems employing ensemble sampling-and-voting strategies (specifically the "Agent Forest" framework) are vulnerable to adversarial input perturbations. While increasing the number of agents ($n \in \{1, \dots, 25\}$) improves accuracy on clean inputs, the system fails to mitigate the impact of synthetic punctuation noise and human-like typographical errors. Attackers can introduce surface-level perturbations—such as random punctuation insertion (10-50%…

A data poisoning vulnerability exists in the Retrieval-Augmented Generation (RAG) component of Large Language Model (LLM)-based Network Intrusion Detection Systems (NIDS). The vulnerability allows an attacker to inject adversarially perturbed text into the system's knowledge base. By employing a transfer-learning attack using a surrogate model (e.g., BERT) and word-level perturbation algorithms (e.g., TextFooler), an attacker can generate semantic-preserving descriptions that alter the vector…

Large Language Models (LLMs), specifically GPT-4o, GPT-4o-mini, LLaMA-2-13B, Mistral-7B, and Phi-3.5-mini, are vulnerable to Man-in-the-Middle (MitM) adversarial prompt injections that undermine factual recall. Termed the "$\chi$mera" (Chimera) attack framework, this vulnerability exists when an attacker intercepts and modifies user queries (e.g., via malicious browser extensions, compromised frontends, or proxy middleware) before they reach the victim model. By appending adversarial…

Multiple open-weight Large Language Models (LLMs)—specifically those prioritizing capability over safety alignment—exhibit a critical vulnerability to adaptive multi-turn prompt injection and jailbreak attacks. While these models effectively reject isolated, single-turn adversarial inputs (averaging ~13.11% Attack Success Rate), they fail to maintain safety guardrails and policy enforcement across extended conversational contexts. By leveraging iterative strategies such as "Crescendo" (gradual…

A vulnerability in Large Language Models (LLMs) allows for systematic jailbreaking through a meta-optimization framework called AMIS (Align to MISalign). The attack uses a bi-level optimization process to co-evolve both the jailbreak prompts and the scoring templates used to evaluate them.

Large Language Models (LLMs) exhibit a vulnerability to "adversarial conversational nudges," where the model abandons its internal factual knowledge to align with user-provided misinformation in closed domains (e.g., movies, books). Unlike standard hallucinations where a model lacks knowledge, this vulnerability occurs even when the model demonstrates—via separate self-consistency checks—that it correctly identifies the information as false. When a user creates a multi-turn context asserting…

Reasoning segmentation models, which generate binary segmentation masks based on implicit text queries, are vulnerable to adversarial paraphrasing. This vulnerability allows an attacker to craft semantically equivalent and grammatically correct text prompts that significantly degrade the model's segmentation performance (measured by Intersection-over-Union, or IoU). The exploit utilizes a black-box, sentence-level optimization method (SPARTA) that operates within the continuous semantic latent…

A jailbreak vulnerability, known as Task Concurrency, exists in multiple Large Language Models (LLMs). The vulnerability arises when two distinct tasks, one harmful and one benign, are interleaved at the word level within a single prompt. The structure of the malicious prompt alternates words from each task, often using separators like {} to encapsulate words from the second task. This "concurrent" instruction format obfuscates the harmful intent from the model's safety guardrails, causing the…

The paper describes a reproducible black-box evaluation and attack framework, PolyJailbreak, for multimodal LLMs. It reports that uneven text-versus-vision safety alignment allows jointly optimized text and image inputs to bypass refusal behavior without model internals. The authors attribute this to visual alignment weakening textual refusal representations and to cross-modal fusion making harmful intent harder to separate from benign intent. These are paper-reported findings, not…