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Last analyzed 9/9/2026

Language Model Security Database

985 research findings · 1123 evaluated models

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228 entries

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

Published 8/24/2026
Analyzed 9/9/2026

A stored interaction can later steer a memory-augmented agent's answer without direct memory-store access. The study evaluates persistent response manipulation in MemoryOS and MemGPT.

InjecMEM: Memory Injection Attack on LLM Agent Memory Systems
Evaluated models: Qwen 2.5 7B Instruct, Qwen 2.5 1.5B Instruct, Qwen 2.5 3B Instruct +3 more

Source: arXiv

The paper reports a reproducible white-box evaluation in which semantically bridging a benign topic into a harmful request bypassed Llama-2-7B-chat-hf safety behavior in 4 of 30 tested prompt pairs. Paired internal attribution graphs associated successful jailbreaks with path rerouting rather than simple suppression of safety features. This is a paper-reported result, not independently verified here. Defensive reproduction should use the paper’s supplied dataset and code in an isolated…

Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
Evaluated models: Llama 2 7B Chat

Source: arXiv

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…

Knowledge Base Poisoning Attacks and Defense for Policy-Aware LLM-RAG Framework
Evaluated models: nomic-embed-text

Source: arXiv

Published 6/26/2026
Analyzed 7/20/2026

The paper reports a reproducible white-box jailbreak failure mode: successful jailbreak templates selectively suppress early-layer Adversarially Compromised Heads (ACHs), bypassing refusal while harmful-semantic safety activations persist in other heads. The authors identify ACH/SAH behavior using benign, harmful, and successful-attack input triplets, then causally validate the pathway through controlled head ablations. Safe defensive reproduction should use the paper’s released evaluation…

Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models
Evaluated models: Llama 3 8B Instruct, Llama 2 7B Chat, Llama 3 70B Instruct

Source: arXiv

The paper presents a concrete, reproducible security evaluation in which attacker-controlled instructions embedded in retrieved external content steer stateful, tool-calling LLM agents toward unauthorized actions. It adapts white-box GCG and black-box TAP to AgentDojo and evaluates single-task and task-universal attacks across 80 task pairs in four domains. The reported results show that semantic black-box optimization can discover functional prompt injections more effectively than…

Assessing Automated Prompt Injection Attacks in Agentic Environments
Evaluated models: Gemma3-4B Instruct, Qwen 3 4B Instruct, GPT-5 +6 more

Source: arXiv

Published 5/27/2026
Analyzed 7/20/2026

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…

SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning
Evaluated models: Llama 2 7B Chat, Mistral 7B Instruct v0.2, Qwen 7B Chat +1 more

Source: arXiv

Published 5/11/2026
Analyzed 7/20/2026

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…

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
Evaluated models: GPT-4o, GPT-5 Chat, Gemini 2.5 Flash +5 more

Source: arXiv

Published 4/30/2026
Analyzed 7/20/2026

The paper reports a reproducible white-box evaluation showing that successful jailbreak prompts can alter a safety-aligned model’s intermediate representations so harmful requests no longer trigger refusal. Its LOCA method identifies small, token-specific residual-stream changes that restore refusal on individual successful jailbreaks, providing causal evidence that jailbreak success can depend on suppressing harmfulness/refusal concepts or strengthening seemingly harmless continuation…

Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
Evaluated models: Gemma 2 2B IT, Llama 3.1 8B Instruct

Source: arXiv

Published 4/1/2026
Analyzed 4/10/2026

A vulnerability in LLM parameter-space merging algorithms enables a latent supply-chain attack where adversaries embed pre-computed malicious weight perturbations into open-source models. Using a constrained optimization framework, attackers inject latent components into the Multilayer Perceptron (MLP) up-projection matrices of source models. These perturbations are mathematically constrained to preserve the source model's individual safety alignment (via directional consistency) and benign…

When Safe Models Merge into Danger: Exploiting Latent Vulnerabilities in LLM Fusion
Evaluated models: Tulu-2-7B, Llama 3.1 Tulu 3 8B DPO, OpenChat 3.5 0106

Source: arXiv

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.