Language Model Security Database

Retrieval-Augmented Generation (RAG) systems are vulnerable to iterative knowledge-extraction attacks designed to reconstruct the underlying private knowledge base. The vulnerability exists due to the decoupled optimization of the retrieval and generation phases. Attackers can craft adversarial queries consisting of two distinct components: an "Information" component (optimized via gradient descent or random sampling to steer embeddings toward specific, diverse regions of the vector space) and…

A vulnerability exists in the post-training alignment of Flow Matching models (specifically FLUX.1-dev) when utilizing Visual Foundation Models (VFM) (e.g., DINOv3b) as discriminators or when employing standalone Reward Gradient optimization (e.g., HPSv3). These feedback mechanisms lack sufficient capacity or structural guidance to constrain the generative policy, making the discriminator's gradients susceptible to "reward hacking." Consequently, the generative policy over-optimizes for the…

Large Language Models (LLMs) exhibit a cross-lingual safety vulnerability driven by a dependency on a sparse subset of "Shared Safety Neurons" (SS-Neurons) anchored in high-resource (HR) languages, typically English. Non-high-resource (NHR) languages lack autonomous safety mechanisms and rely on projecting inputs onto this English-aligned safety manifold to trigger refusals. Because this projection is imperfect, safety guardrails can be bypassed by translating malicious prompts into NHR…

Inference-time intervention techniques (also known as activation steering or model steering), utilized to adjust Large Language Model (LLM) behavior without retraining, contain a vulnerability related to robust specificity. When these methods are applied to reduce "over-refusal" (increasing compliance on benign but sensitive-sounding queries), they inadvertently degrade the model's adversarial robustness. Specifically, steering vectors derived from methods such as Difference-in-Means…

Contrastive Language-Image Pre-training (CLIP) models are vulnerable to semantic-ensemble adversarial attacks. Current adversarial fine-tuning defenses for CLIP rely on minimizing the cosine similarity between an image and a single hand-crafted template (e.g., "A photo of a {label}"). This creates a vulnerability where adversarial examples (AEs) overfit to specific phrasings rather than the core class semantics. Attackers can bypass these defenses by generating semantic-aware adversarial…

LLM-based vulnerability detection systems (used in static application security testing and code review pipelines) are susceptible to semantics-preserving adversarial evasion attacks. Attackers can bypass detection mechanisms by injecting gradient-optimized "universal adversarial strings" into specific code regions—defined as "carriers"—that do not alter the program's compilation or execution logic. These carriers include non-executable regions (code comments, inactive preprocessor directives)…

A vulnerability exists in aligned Large Language Models (LLMs) related to "shallow safety alignment," where safety mechanisms disproportionately rely on the initial tokens generated by the model. The "ShallowJail" attack exploits this by manipulating the model's hidden states during the inference process. Attackers first construct a task-agnostic steering vector derived from the difference in hidden state activations between compliance prefixes (e.g., "Sure, here are the details") and refusal…

A targeted fault-injection vulnerability exists in Large Language Models (LLMs) deployed on hardware susceptible to Rowhammer memory attacks. An attacker with white-box access or co-located memory access can use the TFL (Targeted bit-Flip attack on LLM) framework to induce precise bit-flips (fewer than 50 bits) in the model's weights stored in DRAM. By utilizing a gradient-based search with a keyword-focused attack loss and an auxiliary utility score, the attacker can manipulate the model to…

Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit the positional sensitivity of adversarial tokens. Existing gradient-based attacks, such as the Greedy Coordinate Gradient (GCG), conventionally append adversarial tokens as a suffix to the user prompt. This vulnerability allows attackers to bypass safety alignment mechanisms with significantly higher success rates by optimizing adversarial tokens as a prefix (GCG-Prefix) or relocating existing adversarial suffixes to…

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…