Language Model Security Database

Multiple Text-to-Image (T2I) generation systems and their associated multi-stage moderation pipelines are vulnerable to low-effort semantic obfuscation attacks. Attackers can systematically bypass Input Compliance Checks (ICC), Semantic Safety Checks (SSC), and Post-Generation Moderation (PGM) by embedding restricted concepts into benign natural language contexts. By utilizing techniques such as Material Substitution, Artistic Reframing, Pseudo-Educational Framing, and Ambiguous Action…

Vision-Language Models (VLMs) are vulnerable to pixel-level adversarial image perturbations. An attacker can inject $\ell_p$-bounded, human-imperceptible noise into an input image to manipulate the model's multi-modal embedding space. This reliably causes the VLM to generate incorrect textual responses, hallucinate non-existent objects, or misclassify subjects, effectively decoupling the model's reasoning from the actual visual evidence. The vulnerability is exploitable via both white-box…

An imperceptible visual prompt injection vulnerability in Multimodal Large Language Models (MLLMs) allows attackers to execute precise command-hijacking via a Covert Triggered dual-Target Attack (CoTTA). By embedding a bounded, learnable textual overlay ($L_\infty$ norm bound $\varepsilon \le 16$) and adversarial noise into an input image, the attack forces the source image's internal feature representation to align with both the textual and visual embeddings of an attacker-specified…

A vulnerability in Vision-Language Models (VLMs) relying on shared visual-textual representation spaces allows attackers to induce transferable cross-task semantic failures using an X-shaped Sparse Pixel Attack (XSPA). Attackers craft imperceptible adversarial perturbations restricted to a fixed geometric prior—two intersecting diagonal lines comprising approximately 1.76% of the image pixels. By jointly optimizing a classification objective with cross-task semantic guidance (target-semantic…

Vision-Language-Action (VLA) models are vulnerable to targeted, low-budget textual perturbations in their natural-language instruction inputs, which can maliciously alter sequential decision-making and downstream physical robotic behavior. Because VLA policies tightly couple language, perception, and control, bounded edits—such as character-level typos, token attribute swaps, or prompt-level uncertainty clauses—propagate through the model's execution trajectory. This allows a black-box…

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…

Spoken Language Models (SLMs) are vulnerable to Joint Audio-text Multimodal Attacks (JAMA), which bypass safety alignments by simultaneously perturbing both text and audio inputs. The vulnerability exploits the combined optimization of a discrete text suffix via Greedy Coordinate Gradient (GCG) and a continuous audio perturbation via Projected Gradient Descent (PGD). This joint gradient-based attack pushes the model's hidden layer representations into a distinct subspace far from the benign…

The integration of the visual modality in Large Vision-Language Models (VLMs) introduces a vulnerability where appending an image to a harmful text prompt induces a "jailbreak-related representation shift" in the model's internal high-dimensional space. This shift forcibly steers the model's last-token hidden state away from a designated refusal state and into a distinct jailbreak state. The vulnerability occurs because the visual modality overrides the safety alignment of the underlying…

LLaVA-v1.5-7B, when deployed as a vision-language autonomous agent, is highly vulnerable to adversarial image perturbations. An attacker can inject imperceptibly modified images into a web environment (such as an e-commerce storefront). When the VLM agent captures a screenshot containing the perturbed image, the visual noise forces the model to misclassify the scene and output incorrect, structured JSON actions. This allows an attacker to hijack the agent's task execution, bypassing the user's…

A "Visual Confused Deputy" vulnerability exists in Computer-Using Agents (CUAs) that rely on visual perception to execute coordinate-based GUI actions (e.g., click(x,y)). Because the agent's understanding of the system state is entirely dependent on the screenshot provided by the runtime, a compromised runtime or tool can intercept and alter the screenshot pixels before forwarding them to the LLM. By visually swapping the locations of benign and privileged UI elements, an attacker can trick…