Language Model Security Database

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…

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…

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…

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…

Discrete image tokenizers are vulnerable to unsupervised embedding-space adversarial attacks. Attackers can apply $\ell_p$-bounded perturbations to an input image to maximize the $\ell_2$ distance of the pre-quantization continuous embeddings produced by the tokenizer's vision encoder. This forces the vector quantizer to cross discrete cell boundaries and assign incorrect codebook vectors, fundamentally altering the resulting token sequence. Because the attack targets the pre-quantization…

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 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…

Reinforcement learning (RL) based post-training for explicit chain-of-thought reasoning (e.g., GRPO) in Multimodal Large Reasoning Models (MLRMs) inadvertently degrades safety alignment, rendering the models highly vulnerable to multimodal jailbreak attacks. The vulnerability is caused by "conditional coverage collapse" during the initial phases of chain-of-thought generation. Under adversarial conditioning (text or image), the reasoning policy assigns vanishing probability mass to safe…

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

Large Image Editing Models (LIEMs) supporting vision-prompt editing are vulnerable to Vision-Centric Jailbreak Attacks (VJA). This vulnerability arises from a modality mismatch in safety alignment: while safeguards primarily analyze textual instructions for policy violations, the underlying models are capable of interpreting and executing instructions embedded directly within the visual input (e.g., typographic text drawn on the image, arrows, symbols, or specific markings). An attacker can…