Language Model Security Database

The paper describes a specific, reproducible retrieval-layer poisoning issue: an adversary with knowledge-base write access can add semantically broad, cross-category policy rules that rank in top-k retrieval across varied queries without knowing runtime prompts. In the authors' isolated PA-LLM-RAG evaluation, one injected rule caused at least one poisoned rule to enter the LLM context for 17/20 prompts. Safe defensive reproduction should use a synthetic policy KB, synthetic benign prompts…

SilentRetrieval describes a specific RAG corpus-integrity vulnerability: an attacker able to add a topically relevant document to a retrieval corpus can make that document rank highly and influence the generated answer while remaining fluent enough to evade simple perplexity checks. The paper evaluates a two-stage method combining retrieval-oriented document optimization with context-adaptive claim integration. A safe defensive reproduction is to use only isolated benchmark corpora and inert…

M3Att demonstrates a reproducible knowledge-poisoning issue in medical multimodal RAG: an attacker with limited corpus-distribution knowledge can insert paired image-text entries whose visually perturbed images are broadly retrieved and whose clinically plausible misinformation steers downstream generation. The paper evaluates both white-box and black-box retrieval optimization and reports degraded diagnostic and report-generation utility across multiple datasets, retrievers, and LVLMs. These…

Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…

Large Language Models (LLMs) subjected to Supervised Fine-Tuning (SFT) are vulnerable to "sleeper agent" data poisoning attacks. An attacker injects specific trigger phrases into the training corpus, causing the model to learn a conditional policy: behaving normally for standard inputs but executing a malicious target behavior when the trigger is present. These backdoors persist through safety training and alignment. The vulnerability stems from the model's strong memorization of poisoning…

Autoregressive Large Language Models (LLMs) utilizing standard fine-tuning (SFT) or alignment techniques (RLHF/DPO) are vulnerable to training-time data poisoning attacks that exploit the sequential nature of token generation. Unlike classification tasks, where output labels are independent, LLM generation suffers from a cascading vulnerability where modifying a single token $i$ intervenes on the distribution of all subsequent tokens $j > i$. An adversary can inject a small fraction of…

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…

In distributed Low-Rank Adaptation (LoRA) fine-tuning systems, a structural verification blind spot exists due to the decoupled aggregation of low-rank matrices. Frameworks typically evaluate and aggregate the $A$ and $B$ matrices independently to reduce computational overhead. A malicious client can exploit this by submitting individually benign $A$ and $B$ matrices that satisfy standard norm-based and similarity-based anomaly detection filters, but whose composite product ($A \times B$)…

A malicious model supply chain vulnerability exists involving a technique termed Adversarial Contrastive Learning (ACL) for Large Language Model (LLM) quantization attacks. This vulnerability allows an attacker to publish a model that appears benign and preserves high utility in full precision (e.g., BF16 or FP32) but exhibits malicious behaviors—such as jailbreak, over-refusal, or advertisement injection—immediately upon zero-shot quantization (e.g., INT8, FP4, or NF4).

Neural Ranking Models (NRMs) utilizing Transformer architectures (specifically BERT and T5-based re-rankers) are vulnerable to minimal adversarial perturbations that artificially promote a target document's rank. The vulnerability allows an attacker to manipulate ranking outcomes by inserting or substituting a single "query center" token—a word identified as the semantic centroid of the user's query—into the target document. The attack exploits the model's sensitivity to specific semantic…