Language Model Security Database

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

Agentic LLM systems that automatically preview URLs or extract web metadata are vulnerable to implicit prompt injection, resulting in silent data exfiltration ("silent egress"). Attackers can embed adversarial instructions in unobserved web elements, such as HTML <title> tags, <meta> descriptions, or Open Graph metadata. When a user requests a summary of the URL—or when the agent automatically unfurls a linked URL in a chat—the system fetches the malicious page and flattens this metadata into…

A cryptographic weakness exists in the privacy assumptions of vector embeddings used in Retrieval-Augmented Generation (RAG) systems and Vector Databases. The vulnerability, designated "Zero2Text," allows an unauthenticated attacker to reconstruct raw text from captured vector embeddings without access to the victim model's parameters, gradients, or training data. Unlike prior embedding inversion attacks that require training large decoders on domain-specific datasets, this vulnerability…

Self-evolving Large Language Model (LLM) agents that utilize long-term memory mechanisms (such as Vector Databases for Retrieval-Augmented Generation or Sliding Window buffers) are vulnerable to persistent indirect prompt injection. This vulnerability, termed "Zombie Agent," occurs when the agent's memory update function ($F_M$) processes attacker-controlled content retrieved from external sources (e.g., web pages, documents) and commits it to long-term storage without sufficient sanitization…

The paper reports a black-box jailbreak evaluation in which a ReAct-style loop adaptively rewrites unsafe text prompts and selectively applies blur, DCT filtering, or recoloring to image regions identified as safety-sensitive. The combined cross-modal strategy is intended to make harmful image-text requests appear less objectionable to a vision-language model while preserving enough semantics to elicit an answer. This is a specific, security-relevant evaluation, although the reported results…

The paper describes a specific black-box jailbreak evaluation, BVS, in which fragmented visual content is mixed with neutral imagery and paired with reconstruction-oriented text so harmful intent is only recomposed during multimodal reasoning. The authors report that this can bypass input and output safety assumptions in image-generating MLLMs. A safe defensive reproduction should use synthetic, non-harmful stand-ins for prohibited concepts, test whether fragmented cross-modal inputs are…

The paper describes a reproducible black-box indirect prompt injection evaluation for embedding-based RAG and agent systems. It separates a poisoned document into a retrieval-optimized trigger fragment and an instruction-bearing attack fragment, showing that one injected item can be surfaced by natural queries and then influence model output or agent behavior. These are paper-reported findings; they were not independently verified here.

A vulnerability in AI agent threat detection systems relying on standard conversational tokenization allows attackers to bypass security monitors and execute structural attacks, such as tool hijacking and data exfiltration. Because traditional NLP-based detectors focus on linguistic patterns (surface language) rather than execution flow, an attacker can orchestrate malicious multi-step tool sequences using entirely benign natural language. This structural blindness causes cross-attack…

Conversational Large Language Model (LLM) agents integrated with privileged data sources (e.g., medical records, organizational emails) are vulnerable to contextually inappropriate information disclosure due to failures in enforcing Contextual Integrity (CI) norms. Standard semantic input/output filters and generic safety guardrails (e.g., Llama Guard) fail to detect "mosaic attacks" and multi-turn conversational manipulation. In these attacks, adversaries decompose a malicious query into a…

Large Language Models (LLMs) supporting function calling (tool use) are vulnerable to a jailbreak attack known as iMIST (interactive Multi-step Progressive Tool-disguised Jailbreak Attack). The vulnerability stems from a disparity in alignment training: while models are heavily aligned to refuse harmful natural language generation, they lack sufficient alignment regarding the generation of harmful content within structured data (JSON) used for tool parameters.