Language Model Security Database

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 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…

Reasoning-specialized Large Language Models (LLMs) that utilize Chain-of-Thought (CoT) processes are vulnerable to reasoning-exploitation jailbreaks. Attackers can bypass standard safety alignments (such as RLHF) by using adaptive multi-turn interactions or semantic transformations to induce the model to generate intermediate reasoning steps that "rationalize" or "contextualize" a harmful request. Because current alignment techniques often fail to scale linearly with reasoning depth, forcing…

Large Language Models (LLMs) from multiple vendors are vulnerable to a "poetic jailbreak" attack, a form of stylistic obfuscation where safety guardrails are bypassed by formatting harmful requests as poetry. By encoding prohibited instructions (e.g., malware creation, CBRN protocols) into verse—utilizing metaphors, rhyme schemes, and rhythmic structure—an attacker can evade intent recognition heuristics. The model perceives the input primarily as a creative writing constraint rather than a…

Large Language Models (LLMs) exhibit a vulnerability to "adversarial conversational nudges," where the model abandons its internal factual knowledge to align with user-provided misinformation in closed domains (e.g., movies, books). Unlike standard hallucinations where a model lacks knowledge, this vulnerability occurs even when the model demonstrates—via separate self-consistency checks—that it correctly identifies the information as false. When a user creates a multi-turn context asserting…

Large Language Models (LLMs) are vulnerable to an automated, self-evolving jailbreak attack framework known as ASTRA (Automated Strategy Discovery, Retrieval, and Evolution). This vulnerability exists because current safety alignment mechanisms (such as RLHF) fail to generalize defenses against dynamically distilled and retrieved attack strategies. ASTRA operates on a closed-loop "attack-evaluate-distill-reuse" mechanism. An attacker LLM generates prompts; a separate strategy extractor…

Implementations of Large Language Model (LLM) watermarking algorithms—specifically KGW (Kirchenbauer et al.), Semantic Invariant Robust (SIR) Watermark, Entropy-based Text Watermarking (EWD), and Unbiased Watermarking—are vulnerable to watermark stripping via adversarial text perturbation. When watermarked text generated by models such as OPT-1.3B is subjected to automated paraphrasing or back-translation (e.g., English $\to$ French $\to$ English), the embedded statistical signals are…

LLM-enhanced Graph Neural Networks (GNNs), which integrate Large Language Model (LLM) feature encoders with graph message-passing architectures, are vulnerable to a black-box node injection attack known as "GraphTextack." This vulnerability exists because the joint model architecture creates a dual attack surface: the GNN component is sensitive to structural perturbations (changes in graph topology), while the LLM component is sensitive to semantic perturbations (adversarial phrasing).

Large Language Models are vulnerable to a conceptual manipulation attack, termed Morphology Inspired Conceptual Manipulation (MICM), that bypasses standard safety filters to generate content aligned with harmful extremist ideologies. The attack does not use explicit keywords or standard jailbreak syntax. Instead, it embeds a curated set of seemingly innocuous phrases, called Concept-embedded Triggers (CETs), into a prompt template. These CETs represent an abstract "conceptual configuration" of…

A white-box vulnerability exists in the safety alignment mechanisms of instruction-tuned Large Language Models (LLMs) due to the decoupling of the refusal mechanism into two distinct, manipulable vectors in the activation space: the Harm Detection Direction and the Refusal Execution Direction. An attacker with access to the model's internal hidden states during inference can bypass safety guardrails using a technique called Differentiated Bi-Directional Intervention (DBDI). By intercepting the…