Language Model Security Database

"LLM-as-a-Judge" systems and automated scientific review frameworks (such as OpenReviewer, DeepReview, and institutional deployments like AAAI’s AI-powered assessment) are vulnerable to Indirect Prompt Injection via adversarial PDF manipulation. Attackers can embed hidden instructions—specifically using white font (1pt) or obfuscated semantics—within submission documents. When these PDFs are parsed (typically via OCR or tools like MinerU converting to Markdown) and processed by the LLM, the…

Application-integrated Large Language Models (LLMs) deployed for automated resume screening and candidate ranking are vulnerable to indirect prompt injection via Adversarial Resume Injection. Malicious actors can embed adversarial content—specifically hidden instructions, invisible keywords, or CSS-concealed fabricated experience—within resume documents. When the LLM processes the unstructured resume data alongside structured job requirements, these injections manipulate the model's reasoning…

Large Language Model (LLM) agents utilizing the Model Context Protocol (MCP) are vulnerable to semantic injection attacks via adversarial tool descriptors. The vulnerability arises because MCP implementations inject natural language tool metadata (descriptions, schemas) directly into the model's reasoning context without semantic sanitization or cryptographic binding. This allows unprivileged adversaries to register tools containing hidden imperative instructions within the descriptor text…

Text-to-Video (T2V) diffusion models are vulnerable to black-box adversarial prompt attacks that degrade output quality regarding semantic fidelity and temporal dynamics. This vulnerability is exploited via the T2VAttack framework, which utilizes two primary vector strategies: T2VAttack-S (Substitution) and T2VAttack-I (Insertion). T2VAttack-S leverages a greedy search to identify key semantic tokens and replaces them with high-similarity synonyms defined in lexical databases (e.g., WordNet)…

Reasoning-specialized Large Language Models (LLMs) that utilize Chain-of-Thought (CoT) processes are vulnerable to reasoning-exploitation jailbreaks. Attackers can bypass standard safety alignments (such as RLHF) by using adaptive multi-turn interactions or semantic transformations to induce the model to generate intermediate reasoning steps that "rationalize" or "contextualize" a harmful request. Because current alignment techniques often fail to scale linearly with reasoning depth, forcing…

Large Language Models (LLMs) are vulnerable to an automated, self-evolving jailbreak attack framework known as ASTRA (Automated Strategy Discovery, Retrieval, and Evolution). This vulnerability exists because current safety alignment mechanisms (such as RLHF) fail to generalize defenses against dynamically distilled and retrieved attack strategies. ASTRA operates on a closed-loop "attack-evaluate-distill-reuse" mechanism. An attacker LLM generates prompts; a separate strategy extractor…

A vulnerability, dubbed RoguePrompt, allows for bypassing large language model (LLM) moderation filters by encoding a forbidden instruction into a self-reconstructing payload. The attack uses a dual-layer ciphering process. First, the forbidden prompt is partitioned into two subsequences (e.g., even and odd words). One subsequence is encrypted using a classical cipher like Vigenere, while the other remains plaintext. Both the plaintext subsequence, the Vigenere ciphertext, and natural language…

Large Language Models are vulnerable to a conceptual manipulation attack, termed Morphology Inspired Conceptual Manipulation (MICM), that bypasses standard safety filters to generate content aligned with harmful extremist ideologies. The attack does not use explicit keywords or standard jailbreak syntax. Instead, it embeds a curated set of seemingly innocuous phrases, called Concept-embedded Triggers (CETs), into a prompt template. These CETs represent an abstract "conceptual configuration" of…

A jailbreak vulnerability, termed Embedded Jailbreak Template (EJT), allows for the generation of harmful content by bypassing the safety mechanisms of Large Language Models (LLMs). The attack uses a generator LLM to contextually integrate a harmful query into a pre-existing jailbreak template. Unlike fixed templates which insert a query into a static placeholder, EJT rewrites multiple parts of the template to embed the harmful intent naturally. This process preserves the original template's…

OpenVLA, a Vision-Language-Action (VLA) model, contains a vulnerability regarding multimodal adversarial robustness. The model lacks sufficient cross-modal alignment stability, allowing attackers to disrupt the grounding between visual perception and linguistic instructions. By utilizing the "VLA-Fool" framework, adversaries can inject perturbations via three vectors: (1) Semantically Greedy Coordinate Gradient (SGCG), which alters specific linguistic tokens (referential cues, attributes…