Language Model Security Database

Activation steering techniques, such as Contrastive Activation Addition (CAA), systematically erode the safety alignment of Large Language Models (LLMs) due to geometric interference within the residual stream. Steering vectors intended to modulate benign or utility-driven behaviors (e.g., sycophancy, openness, self-awareness) often exhibit a negative cosine similarity with the model's latent 1D refusal direction. When applied during inference, these steering vectors inadvertently suppress the…

LLM-based autonomous email security agents configured with signal-based system prompts are vulnerable to a "signal inversion" attack via infrastructure phishing. When a system prompt instructs an LLM to prioritize a specific heuristic—such as sender-URL domain consistency—attackers can bypass the security filter entirely by registering a single, inexpensive domain and using it for both the sender email address and the malicious payload host. Because the LLM faithfully executes the prioritized…

A vulnerability in the safety alignment of Large Language Models (LLMs) allows attackers to bypass safety guardrails by using malicious prompts contextualized in the Thai language and culture. Evaluated models exhibit a significantly higher Attack Success Rate (ASR) against Thai-specific, culturally contextualized attacks compared to general translated attacks. By exploiting local cultural nuances, regional slang, and Thai socio-cultural contexts, attackers can easily circumvent standard…

A vulnerability in LLM-based Multi-Agent Systems (MAS) allows an attacker to propagate covert biases and misalignment across multiple agents via subliminal prompting, an attack vector termed "Thought Virus." By injecting a seemingly benign, semantically unrelated token (such as a specific 3-digit number) into the prompt of a single compromised agent, an attacker can induce a specific targeted behavior (e.g., outputting a specific target concept or decreasing factual truthfulness). This induced…

Transformer-based Large Language Models (LLMs) are vulnerable to highly query-efficient black-box jailbreak attacks due to the structural properties of refusal behaviors: skewed token contribution and cross-model consistency. Refusal mechanisms within LLMs are typically triggered by a sparse subset of sensitive tokens rather than the entire prompt, and these refusal representations (specifically the primary left singular vector of the perturbed representation matrix at intermediate layers) are…

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…

Large Language Models (LLMs) are vulnerable to TAO-Attack, an advanced optimization-based jailbreak that bypasses safety alignments by exploiting gradient-guided token updates. The vulnerability stems from a two-stage loss function combined with a Direction-Priority Token Optimization (DPTO) algorithm. In the first stage, the attack optimizes an adversarial prompt suffix to minimize the probability of refusal signals (e.g., "I cannot") while maximizing the probability of a harmful target…

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 "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…

Frontier Multimodal Large Language Models (MLLMs) are vulnerable to Visual Exclusivity (VE) attacks, an "Image-as-Basis" threat where malicious intent is achieved through joint reasoning over benign text and complex technical visual content (e.g., blueprints, schematics, network diagrams). Unlike wrapper-based attacks that conceal malicious text via typography or adversarial noise, VE exploits the model's core visual reasoning capabilities. Attackers can bypass safety filters by combining…