Language Model Security Database

A vulnerability exists in tool-augmented Large Language Model (LLM) agents characterized as "Tag-Along Attacks," where an unprivileged external user (or adversarial agent) coerces a safety-aligned Operator agent into executing prohibited tool calls. Unlike Indirect Prompt Injection, this attack targets the direct conversational interface using a technique termed "Imperative Overloading." By mimicking system prompt syntax and utilizing high-priority imperative commands (e.g., "Strict adherence…

Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit the positional sensitivity of adversarial tokens. Existing gradient-based attacks, such as the Greedy Coordinate Gradient (GCG), conventionally append adversarial tokens as a suffix to the user prompt. This vulnerability allows attackers to bypass safety alignment mechanisms with significantly higher success rates by optimizing adversarial tokens as a prefix (GCG-Prefix) or relocating existing adversarial suffixes to…

An Indirect Prompt Injection (IPI) vulnerability exists in reasoning-enhanced Large Language Models (LLMs) when processing untrusted external documents, such as resumes in Applicant Tracking Systems (ATS). Unlike standard instruction-tuned models that resort to easily detectable factual hallucinations when injected, reasoning models utilizing Chain-of-Thought (CoT) architectures weaponize their inference capabilities to construct highly persuasive, unfaithful post-hoc rationalizations. They…

Vision-Language Models (VLMs) are vulnerable to a universal and transferable adversarial attack dubbed "UltraBreak." This vulnerability allows remote attackers to bypass safety alignment filters and elicit harmful responses (e.g., hate speech, dangerous instructions) by supplying a single, specifically crafted adversarial image alongside a text query. Unlike traditional gradient-based attacks that optimize for specific token sequences (cross-entropy loss) and result in brittle…

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…

Vision Language Models (VLMs) utilizing independent vision encoders (e.g., ViT) and Large Language Model (LLM) decoders are vulnerable to Split-Image Visual Jailbreak Attacks (SIVA). The vulnerability arises from an architectural and alignment discrepancy: while the vision encoder processes image fragments (splits) in isolation via constrained attention or block-diagonal masks, the LLM decoder aggregates these features via cross-attention to reconstruct the semantic content. Current safety…

A vulnerability in Large Vision-Language Models (LVLMs) allows attackers to bypass safety guardrails via a Multi-Turn Adaptive Prompting Attack (MAPA). Instead of triggering safety mechanisms with an immediate, explicit malicious request, the attacker iteratively injects malicious intent across multiple conversation turns by alternating between text and visual modalities. At each turn, the attack dynamically tests three prompt configurations (unconnected text only, unconnected text + malicious…

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…