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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

Filtered research findings

3 entries

Matches every word across titles, descriptions, sources, affected systems, and models.

LLM Judge Manipulation

Published Analyzed Source: arXiv

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…

Security in LLM-as-a-Judge: A Comprehensive SoK
Evaluated models: GPT-4o, o1, Qwen 2.5 72B Instruct+1 more
  • GPT-4o
  • o1
  • Qwen 2.5 72B Instruct
  • Llama 3 70B Instruct
Model layer
Application layer
Prompt layer
Injection

Unified Robustness Gap

Published Analyzed Source: arXiv

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…

Unifying Adversarial Robustness and Training Across Text Scoring Models
Evaluated models: E5 BERT-base, Qwen 3 0.6B, Llama 3.2 3B Instruct+2 more
  • E5 BERT-base
  • Qwen 3 0.6B
  • Llama 3.2 3B Instruct
  • Llama 3.1 8B Instruct
  • Skywork Reward V2
Model layer
Prompt layer
Injection
Poisoning

STEP Manufacturing Corruption

Published Analyzed Source: arXiv

The STEP-LLM framework, utilized for generating Computer-Aided Design (CAD) STEP files (ISO 10303) from natural language, exhibits a safety alignment vulnerability during the fine-tuning of base Large Language Models (specifically Llama-3.2-3B-Instruct and Qwen-2.5-3B). The training pipeline employs Depth-First Search (DFS) reserialization and Reinforcement Learning (RL) with Scaled Chamfer Distance rewards to optimize for geometric fidelity and syntactic validity of Boundary Representation…

STEP-LLM: Generating CAD STEP Models from Natural Language with Large Language Models
Evaluated models: GPT-4o, Llama 3.2 3B, Qwen 2.5 3B
Model layer
RAG
Fine-tuning
Hallucination

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.