Language Model Security Database

Speech-driven Large Language Models (LLMs) and end-to-end Large Audio-Language Models (LALMs) are vulnerable to inaudible near-ultrasonic prompt injections, a framework dubbed Sirens' Whisper (SWhisper). By exploiting the non-linear response of commodity microphones, attackers can encode structured, phonetically optimized adversarial prompts into the 17–22 kHz near-ultrasonic band. Using regularized channel-inversion pre-compensation, the attacker shapes the waveform to account for microphone…

A vulnerability in the compositional reasoning architecture of Large Vision-Language Models (LVLMs) allows attackers to bypass multimodal safety alignments using a technique known as Reasoning-Oriented Programming (ROP). Current safety mechanisms primarily target explicit malicious patterns at the perception level (early layers). This vulnerability exploits late-stage reasoning by decomposing a harmful objective into a set of spatially isolated, semantically benign visual "gadgets". Because…

A semantic slot filling vulnerability in Large Vision-Language Models (LVLMs) allows attackers to bypass safety filters and elicit prohibited content via a single query. The attack, known as StructAttack, decomposes a harmful instruction into a central topic and locally benign-appearing semantic slot types (e.g., "Raw Materials", "Making Process"). These individual slots are embedded into structured visual prompts (such as mind maps, tables, or sunburst diagrams) alongside harmless distractor…

A temporal trajectory infilling vulnerability in Text-to-Video (T2V) generative models allows attackers to bypass input and output safety filters to generate policy-violating content. The vulnerability is exploited using a fragmented prompting technique known as Two Frames Matter (TFM). An attacker submits a prompt that specifies only sparse boundary conditions (the start and end frames) using semantically suggestive but lexically benign alternatives, entirely omitting the intermediate action…

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…

Multimodal Large Language Models (LLMs) are vulnerable to alignment bypass via Inter-Turn Modality Switching (ITMS). By systematically rotating the input modality (e.g., alternating between text, audio, and image) across successive turns in a multi-turn adversarial conversation, an attacker can destabilize the model's safety defenses. The cross-modal transition mechanism exploits alignment gaps between differing input processing pipelines, accelerating the erosion of safety guardrails and…

Video-based Large Language Models (Video-LLMs) are vulnerable to a universal Energy-Latency Attack (ELA) that triggers a Denial-of-Service (DoS) via spatially concentrated adversarial patches. Because video architectures rely on temporal subsampling and pooling which act as a low-pass filter against full-frame diffuse noise, an attacker can bypass this compression by anchoring cross-modal attention to a dense, localized visual anomaly. By injecting a fixed, content-agnostic patch into the…

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…

The paper evaluates a reproducible indirect prompt injection issue in ReAct-style LLM agents: untrusted retrieved content can be interpreted as instructions and redirect the agent toward unauthorized tool calls. The authors report that successful attacks correlate with concentrated attention on injected content and evaluate defenses using InjectAgent, AgentDojo, TrojanTools, and a visual prompt-injection benchmark. These are paper-reported findings, not independently verified facts.

A vulnerability in multi-category safety-guidance mechanisms (such as Safe Latent Diffusion [SLD] and SAFREE) for Text-to-Image (T2I) diffusion models allows attackers to bypass safety filters and generate restricted content via "Harmful Conflicts." Existing safety methods aggregate multiple harmful keyword categories (e.g., hate, violence, sexual) into a single unified safety direction in the latent or text space. Because distinct harmful categories possess incompatible safety directions…