Language Model Security Database

An evasion vulnerability in Text-Attributed Graph (TAG) learning models allows attackers to induce targeted misclassifications via LLM-generated, coordinated perturbations to both graph topology and textual semantics. By identifying a semantically distant "influencer" node, an attacker can use a separate LLM to selectively delete highly relevant edges, insert a deceptive edge connecting the target to the influencer, and slightly modify the target node's text to include a keyword aligned with…

Large Language Models (LLMs) are vulnerable to automated long-tail distribution attacks that exploit their instruction-following and code-execution capabilities to bypass safety alignments. Attackers can obfuscate malicious queries using a semantic-algorithmic representation, embedding the query within reversible encryption-decryption logic (e.g., sequence re-grouping, conditional branching, or index-dependent operations). By providing the model with the encrypted query and the corresponding…

Multi-Modal Large Language Models (MLLMs) are vulnerable to a highly transferable, black-box adversarial image attack known as the Multi-Paradigm Collaborative Attack (MPCAttack). Attackers can craft imperceptible visual perturbations by jointly aggregating and optimizing semantic feature representations extracted from surrogate models across three distinct learning paradigms: cross-modal alignment (e.g., CLIP), multi-modal understanding (e.g., InternVL3), and visual self-supervised learning…

A vulnerability in Multimodal Large Language Models (MLLMs) allows attackers to bypass safety alignments via Multi-Image Dispersion and Semantic Reconstruction (MIDAS). Attackers decompose malicious instructions into risk-bearing semantic subunits, fragment them, and distribute them across multiple benign-looking Game-style Visual Reasoning (GVR) puzzles (e.g., Letter Equations, Rank-and-Read, Odd-One-Out). A sanitized, persona-driven textual prompt with sequential placeholders is then used to…

Large Vision-Language Models (LVLMs) are vulnerable to multi-turn, multi-modal jailbreak attacks where malicious intent is incrementally introduced and obfuscated through intertwined text and image prompts. Attackers can systematically bypass safety alignments by starting with self-optimized, benign-seeming conversation starters (horizontal expansion) and progressively stacking text and image attack augmentations across multiple conversation turns (vertical expansion). Furthermore, models fail…

A vulnerability in Large Language Models (LLMs) equipped with built-in "thinking" or step-by-step reasoning modes allows attackers to bypass safety alignments, trigger reasoning collapse, and cause resource exhaustion. The vulnerability is exploited via a Multi-Stream Perturbation Attack, which fragments the sequential integrity of a harmful prompt by word-by-word interleaving it with benign auxiliary tasks (e.g., "Explain the water cycle"). By wrapping the benign text streams in specific…

Claude Opus 4.6, Gemini 3.1 Pro, and GPT-5.2 are vulnerable to safety guardrail bypasses via authoritative and operational contextual framing. Attackers can evade safety classifiers by encapsulating restricted objectives (e.g., malicious code generation, misinformation, social engineering) within "legitimate" professional contexts, such as graduate-level academic research, network stress-testing, or corporate security awareness simulations. This vulnerability is exploitable both via zero-shot…

LLM-based autonomous agents deployed in multi-turn, structured environments are vulnerable to adaptive, profit-driven semantic exploitation. Rather than utilizing traditional malformed prompt injections or jailbreaks, an attacker can leverage valid interaction channels to execute social engineering, protocol spoofing, and authority impersonation tactics. By strategically shaping the environment's context—such as feigning technical constraints, fabricating evaluation harnesses, or manipulating…

A vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to achieve an exponentially scaling Attack Success Rate (ASR) for jailbreaks by combining adversarial prompt injection with repeated inference-time sampling. While ASR against un-injected prompts scales polynomially with the number of generated samples ($k$), introducing a long adversarial suffix acts as a strong "misalignment field." This shifts the model's generation distribution into a replica-symmetric ordered…

A compound vulnerability in Retrieval-Augmented Generation (RAG) systems allows attackers to deterministically hijack model outputs for arbitrary user queries without prior knowledge of the user's input. The vulnerability, identified as PIDP-Attack, requires a dual-vector exploitation: database poisoning and query-path prompt injection. First, the attacker injects a small number of poisoned passages into the RAG database, each starting with an attacker-chosen "target question" followed by a…