Language Model Security Database

Inference-time Personally Identifiable Information (PII) leakage is significantly amplified when using Chain-of-Thought (CoT) prompting or reasoning-enabled Large Language Models (LLMs). When an attacker or user elicits step-by-step reasoning or utilizes models with native "thinking" token budgets, sensitive context data provided in the prompt is directly resurfaced into intermediate reasoning steps or the final output. This bypasses output-level privacy policies instructing the model not to…

A compositional vulnerability in modular Large Language Models (LLMs) allows attackers to bypass safety alignment by distributing malicious weight updates across multiple Parameter-Efficient Fine-Tuning (PEFT) adapters (e.g., LoRA). The malicious adapters are anchored to valid functional subspaces (e.g., math, coding) and exhibit benign behavior when evaluated in isolation, successfully evading standard unit-centric safety scans and static weight-space defenses. However, when a user linearly…

An "in-content harm" vulnerability exists in safety-aligned Large Language Models (LLMs) where task-level alignment mechanisms fail to evaluate the safety of user-provided external data. Attackers can bypass safety guardrails by embedding policy-violating text (e.g., violence, self-harm, explicit content) within the payload of a seemingly benign, policy-compliant task (e.g., translation, summarization, grammar polishing). Because the primary instruction is harmless, the LLM's safety filters…

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…

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…

An activation-steering vulnerability in open-weights Large Language Models allows attackers to bypass safety guardrails by injecting targeted additive perturbations into deep, safety-critical attention heads. The exploit, termed Safety Attention Head Attack (SAHA), utilizes Ablation-Impact Ranking (AIR) to isolate specific attention heads that causally govern safety refusals. By applying Layer-Wise Perturbations (LWP) derived from the linearized decision boundary of a latent safety probe, an…

Safety-aligned Large Language Models (LLMs) exhibit a "Defensive Refusal Bias" vulnerability, resulting in a safety-induced denial-of-service for legitimate cybersecurity operations. The models systematically refuse authorized defensive queries when they contain security-sensitive terminology (e.g., "exploit," "payload," "shell") because current alignment mechanisms rely on semantic similarity to harmful training data rather than intent analysis. Paradoxically, explicit authorization signals…

A prompt structure vulnerability exists in instruction-tuned Large Language Models (LLMs) where attackers can bypass safety alignments by injecting a continuation-triggering suffix immediately following the user prompt termination token. By placing an affirmative suffix outside the user instruction boundary, it is processed as the beginning of the assistant's own pre-filled response. This structural manipulation intrinsically overactivates the model's continuation attention heads, forcing its…

Embodied Large Language Models (LLMs) used for real-world agent planning are vulnerable to Action-level Manipulation (dubbed "Blindfold"), a jailbreak technique that bypasses semantic-level safety filters by exploiting the models' limited spatial and causal reasoning regarding physical consequences. Attackers can use an adversarial proxy LLM to decompose a semantically harmful intent into a sequence of individually benign primitive actions. To evade advanced semantic correlation checks…

Internal Safety Collapse (ISC) is a vulnerability in frontier Large Language Models (LLMs) where models autonomously generate highly restricted, harmful content while executing structurally legitimate professional workflows. The vulnerability triggers when a model infers that generating sensitive data is a functional requirement to complete an otherwise benign task. By nesting harmful content generation inside standard execution constraints (e.g., resolving a schema validation error in a…