Language Model Security Database

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 safety bypass vulnerability, dubbed "Ninja" (Needle-in-a-haystack jailbreak), exists in long-context Large Language Models (LLMs). The vulnerability exploits a degradation in safety alignment that occurs when a harmful goal is embedded within a massive, benign context window. Unlike traditional adversarial attacks that use unintelligible strings or "many-shot" attacks that use harmful examples, this method utilizes thematically relevant but innocuous text (the "haystack"). The attack…

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 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…

The KG-DF (Knowledge Graph Defense Framework) contains a logic vulnerability in its Semantic Parsing Module, specifically within the keyword extraction phase defined as $K_{core} = \text{LLM}(P_{prompt})$. The framework relies on a Large Language Model (e.g., GPT-3.5-turbo) to distill user input into keywords ($K_{core}$), which are then embedded to retrieve security warning triples ($T_{match}$) from a Knowledge Graph.

Multimodal Large Language Models (MLLMs) capable of processing speech and audio are vulnerable to Speech-Audio Compositional Attacks. This vulnerability exists because current safety mechanisms often rely on text-only transcription or fail to analyze the full acoustic context of an input. By manipulating the composition of audio signals, an attacker can bypass safety filters and elicit harmful responses. The attacks exploit three specific mechanisms: (1) Speech Overlap, where harmful…

A vulnerability exists in multiple Large Language Models (LLMs) that allows for safety alignment bypass through an advanced jailbreaking technique called Template and Suffix Optimization (TASO). The attack combines two distinct optimization methods in an alternating, iterative feedback loop. First, a semantically meaningless adversarial suffix is optimized (e.g., using gradient-based methods like GCG) to force the LLM to begin its response with an affirmative phrase (e.g., "Sure, here is...")…

Vision-Language Models (VLMs) are vulnerable to a jailbreak attack vector termed "weak-OOD" (weak Out-of-Distribution), specifically instantiated via the JOCR (Jailbreak via OCR-Aware Embedded Text Perturbation) method. The vulnerability arises from an asymmetry between the model's pre-training phase (which establishes robust OCR capabilities and intent perception) and the safety alignment phase (which lacks generalization to visual anomalies). Attackers can embed malicious text instructions…

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…

Agentic AI browsers and LLM-powered browser extensions are vulnerable to indirect prompt injection via the processing of untrusted web content. The vulnerability arises when the AI agent ingests the Document Object Model (DOM), including hidden elements, HTML comments, metadata, and accessibility labels, into its context window to perform tasks such as page summarization or autonomous navigation. Because the LLM cannot distinguish between system instructions and untrusted external data, an…