Language Model Security Database

Application-integrated Large Language Models (LLMs) deployed for automated resume screening and candidate ranking are vulnerable to indirect prompt injection via Adversarial Resume Injection. Malicious actors can embed adversarial content—specifically hidden instructions, invisible keywords, or CSS-concealed fabricated experience—within resume documents. When the LLM processes the unstructured resume data alongside structured job requirements, these injections manipulate the model's reasoning…

Large Language Model (LLM) agents operating in tool-augmented environments are susceptible to "Contextual Fragility" and multi-turn "long-chain" exploitation. Existing safety mechanisms predominantly function on a stateless, atomic paradigm, evaluating individual input-output pairs in isolation. This allows an adversary to orchestrate complex attack trajectories where malicious intent is distributed across multiple, individually benign steps (a "Domino Effect"). Consequently, an attacker can…

Large Language Models (LLMs) are vulnerable to a multi-turn adversarial attack framework termed MEEA (Mere Exposure Effect Attack), which exploits the psychological "mere exposure effect" to bypass safety alignment. Unlike single-turn injections, this vulnerability targets the dynamic nature of LLM safety thresholds during sustained interaction. By subjecting the model to a sequence of optimized, low-toxicity, and semantically progressive prompts, an attacker can induce a gradual shift in the…

Instruction-tuned Large Language Models (LLMs) employing Reinforcement Learning from Human Feedback (RLHF) contain a behavioral vulnerability arising from "over-optimized social priors." This vulnerability, termed Psychological Jailbreak, allows attackers to bypass safety guardrails by exploiting the model’s optimization for anthropomorphic consistency. By establishing a Structured Persona Context (SPC) that aligns with latent psychometric traits (e.g., high agreeableness or neuroticism), an…

Large Language and Vision Assistant (LLaVA) v1.5-13B and Meta Llama 3.2 11B Vision are vulnerable to adversarial evasion attacks targeting the visual input modality. An attacker with white-box access (knowledge of model architecture and gradients) can employ Projected Gradient Descent (PGD) to generate adversarial perturbations constrained by an L-infinity norm. By maximizing the model's internal loss function with respect to the input image, the attacker can force the Vision-Language Model…

A vulnerability exists in MetaGPT's DataInterpreter agent (and similar RAG-based agents utilizing persistent long-term memory) that allows for persistent memory poisoning via indirect injection. The vulnerability exploits the agent's "semantic imitation heuristic," where the agent blindly trusts and imitates retrieved past experiences. An attacker can supply a benign-looking artifact (e.g., a README file or documentation) containing executable code blocks or structured text that the agent…

A jailbreak vulnerability in Large Language Models (LLMs) allows attackers to bypass safety constraints by framing harmful requests as structural narrative analysis tasks based on Vladimir Propp’s morphology of folktales. Known as "Adversarial Tales," the attack embeds prohibited instructions (e.g., cyberattack methodologies or restricted synthesis steps) within a fictional narrative, typically using a cyberpunk setting. The user then prompts the model to decompose the story using specific…

Open-weight Large Language Models (LLMs) are vulnerable to a white-box safety alignment bypass known as "abliteration" (directional orthogonalization). An attacker with access to the model weights can compute the "refusal direction" in the residual stream activation space by contrasting internal activations between harmful and harmless prompts. By projecting the model's weight matrices to be orthogonal to this single direction (or specific concept cones), the safety alignment is surgically…

Large Language Models (LLMs) finetuned from open-weight pretrained sources inherit adversarial vulnerabilities encoded in the pretrained model's internal representations. An attacker with white-box access to a pretrained model (e.g., Llama-2, Llama-3) can identify linearly separable features in the hidden states that correlate with "transferable" jailbreak prompts. By exploiting these features using a Probe-Guided Projection (PGP) attack, the attacker can optimize adversarial suffixes on the…

"LLM-as-a-Judge" systems and automated scientific review frameworks (such as OpenReviewer, DeepReview, and institutional deployments like AAAI’s AI-powered assessment) are vulnerable to Indirect Prompt Injection via adversarial PDF manipulation. Attackers can embed hidden instructions—specifically using white font (1pt) or obfuscated semantics—within submission documents. When these PDFs are parsed (typically via OCR or tools like MinerU converting to Markdown) and processed by the LLM, the…