Language Model Security Database

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…

Leading Large Language Models (LLMs) exhibit significant cross-lingual safety drift, allowing users to bypass safety guardrails by translating harmful prompts into low-resource Indic languages. While models effectively block unsafe prompts concerning caste, religion, gender, and politics in high-resource languages like English and Hindi, their safety alignment severely degrades in low-resource scripts such as Odia, Telugu, Kannada, and Punjabi. Evaluated models demonstrate a cross-language…

Agentic Large Language Model (LLM) systems utilizing persistent memory, Retrieval-Augmented Generation (RAG) pipelines, and external tool connectors are vulnerable to Logic-layer Prompt Control Injection (LPCI). An attacker can inject obfuscated (e.g., encoded, structurally nested, or semantically reframed) payloads into external memory stores or RAG documents. These payloads bypass conventional inference-time plaintext content filters, persist across session boundaries, and remain dormant…

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…

A vulnerability in multi-step, tool-using Large Language Model (LLM) agents allows attackers to bypass safety guardrails by manipulating user context variables, such as personalization profiles or persistent memory. The safety policies of frontier LLMs are highly context-dependent; inserting innocuous user bios (e.g., demographic or health disclosures) fundamentally alters the agent's action policy. When combined with lightweight adversarial jailbreaks, specific personalization contexts…

A structural vulnerability in the safety alignment of Large Language Models (LLMs) allows attackers to bypass guardrails by manipulating when and how malicious intent is reconstructed during inference. This vulnerability, exploited via Structured Semantic Cloaking (S2C), takes advantage of safety mechanisms that rely on the coherent, explicit surface realization of harmful semantics at early generation stages. By structurally fragmenting malicious queries across disjoint prompt segments and…

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…

Large Language Models (LLMs) are vulnerable to a jailbreak technique termed "Priority Hacking." Adversaries can bypass safety alignments by exploiting the model's internal priority graph, where certain abstract values (e.g., justice, public health) implicitly outweigh general safety restrictions within specific contexts. By crafting a deceptive prompt that frames a malicious request as a necessary action in service of a higher-priority benign value, attackers engineer a value conflict. The…

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…

Autoregressive language models are vulnerable to state-dependent safety collapse via structured multi-turn context manipulation. The vulnerability stems from the model treating dialogue history as a state transition operator rather than a passive record. By initializing a conversational trajectory with a semantic-preserving softened query and a query-aware persona containing specific named entities, an attacker can establish "representational anchors" that trigger abrupt phase transitions in…