{"entries":[{"title":"Boilerplate safety tuning causes false refusals on benign requests","cveId":"cba592a9","paperTitle":"Refuse without Refusal: A Structural Analysis of Safety-Tuning Responses for Reducing False Refusals in Language Models","paperUrl":"https://arxiv.org/abs/2609.04714","paperDate":"2026-09-04","analysisDate":"2026-09-09T16:04:32.326Z","tags":["model-layer","jailbreak","fine-tuning","safety","reliability"],"affectedModels":["Llama 3.1 8B","Mistral 7B v0.3","Gemma 2 9B","Qwen 2.5 7B","Gemma 2 2B","Qwen 2.5 3B","Llama 3.1 70B","Qwen 2.5 72B","Llama 3.1 8B Instruct","Mistral 7B Instruct v0.3","Gemma 2 9B IT","Qwen 2.5 7B Instruct"],"description":"Controlled safety-tuning experiments link boilerplate refusal statements to unnecessary refusals of benign requests. Request-specific rationales improve benign compliance, with benchmark-dependent safety tradeoffs.","slug":"boilerplate-safety-tuning-causes-false-refusals-on-benign-requests","hasDedicatedEvidence":true},{"title":"Deleted agent memory persists in live execution state","cveId":"0d369694","paperTitle":"Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents","paperUrl":"https://arxiv.org/abs/2609.04875","paperDate":"2026-09-04","analysisDate":"2026-09-09T16:02:20.859Z","tags":["application-layer","extraction","agent","memory","data-privacy","data-security","integrity","reliability"],"affectedModels":["Llama 3.1 8B Instruct","Qwen 2.5 7B Instruct","Mistral 7B Instruct v0.3"],"description":"Deleting a memory record can leave its information in an agent's summaries, pending plans and KV cache. The paper evaluates revocation across this derived execution state.","slug":"deleted-agent-memory-persists-in-live-execution-state","hasDedicatedEvidence":true},{"title":"Document reconstruction from exposed retrieval embeddings","cveId":"2021f881","paperTitle":"Shadow Queries for Private Retrieval in Vector Databases","paperUrl":"https://arxiv.org/abs/2609.04767","paperDate":"2026-09-04","analysisDate":"2026-09-09T16:03:48.163Z","tags":["application-layer","extraction","rag","embedding","data-privacy","blackbox"],"affectedModels":["QwQ 32B"],"description":"Exposed retrieval embeddings can reveal source text. SHAQ evaluates indexing generated queries instead of direct document embeddings to reduce that leakage.","slug":"document-reconstruction-from-exposed-retrieval-embeddings","hasDedicatedEvidence":true},{"title":"Selective refusal gaps in visual question answering","cveId":"41ff4d29","paperTitle":"Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models","paperUrl":"https://arxiv.org/abs/2609.04720","paperDate":"2026-09-04","analysisDate":"2026-09-09T16:05:10.221Z","tags":["model-layer","vision","multimodal","safety","reliability","hallucination","fine-tuning"],"affectedModels":["InternVL3 2B Instruct","InternVL3-78B-Instruct","Qwen 2.5 VL 3B Instruct","Qwen 2.5 VL 72B Instruct","GPT-5","Gemini 2.5 Flash","InternVL3-2B-KoNA","Qwen2.5-VL-3B-KoNA"],"description":"KoNA measures whether vision-language models answer valid image questions while refusing unsafe components or correcting unsupported premises. Its 9,300 question-answer pairs include mixed and fully answerable controls.","slug":"selective-refusal-gaps-in-visual-question-answering","hasDedicatedEvidence":true},{"title":"Visual prompt injection crosses document trust boundaries","cveId":"f34622ab","paperTitle":"Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection","paperUrl":"https://arxiv.org/abs/2609.04533","paperDate":"2026-09-03","analysisDate":"2026-09-09T16:03:01.481Z","tags":["application-layer","injection","vision","multimodal","blackbox","agent","data-privacy","integrity","prompt-layer"],"affectedModels":["claude-opus-4-7","GPT-5.5","Gemini 3.1 Pro","Qwen3.6-27B","Qwen3-VL 32B Instruct","InternVL3.5 38B Instruct"],"description":"Untrusted document images can redirect vision-language agents across instruction and tool-authorization boundaries. Repeat-After-Me evaluates six victim models on constructed document tasks.","slug":"visual-prompt-injection-crosses-document-trust-boundaries","hasDedicatedEvidence":true},{"title":"PII leakage in document-image redaction benchmarks","cveId":"1b9be65b","paperTitle":"LeakageBench: Document-Level Leakage Risk for Redacting Personally Identifiable Information in Document Images","paperUrl":"https://arxiv.org/abs/2609.02207","paperDate":"2026-09-02","analysisDate":"2026-09-09T16:00:45.299Z","tags":["application-layer","vision","multimodal","data-privacy","data-security","reliability"],"affectedModels":["GLiNER-base","GLiNER-multi-PII","NVIDIA GLiNER PII","GLiNER2","Qwen3-VL-32B","InternVL3 38B","GPT-5.4","GPT-5.5"],"description":"Document-image PII detection can miss identifiers despite improving average localization scores. LeakageBench evaluates 500 pages with 11,954 annotations.","slug":"pii-leakage-in-document-image-redaction-benchmarks","hasDedicatedEvidence":true},{"title":"Context privilege escalation in AI agent harnesses","cveId":"ea65671d","paperTitle":"What's in Your Agent's Context? Context Privilege Escalation Attacks against AI Agent Harness","paperUrl":"https://arxiv.org/abs/2609.01222","paperDate":"2026-09-01","analysisDate":"2026-09-09T16:01:21.448Z","tags":["application-layer","prompt-layer","injection","agent","coding-agent","integrity","data-security","memory","denial-of-service"],"affectedModels":["GPT-5.5","GPT-5.4-mini","Claude Sonnet 4.6","Claude Opus 4.6","Gemini 2.5 Flash","Gemini 2.5 Pro","DeepSeek V4 Flash"],"description":"Context assembly can promote repository, tool or skill content into higher-priority instructions or persistent state. The paper studies 12 pinned agent-harness versions.","slug":"context-privilege-escalation-in-ai-agent-harnesses","hasDedicatedEvidence":true},{"title":"Cross-user data isolation in shared GPU serving","cveId":"a843fd94","paperTitle":"Here is a GIFT: Enforcing User Data Isolation in LLM Serving via GPU Information Flow Tracking","paperUrl":"https://arxiv.org/abs/2608.25431","paperDate":"2026-08-26","analysisDate":"2026-09-09T16:00:06.751Z","tags":["infrastructure-layer","data-privacy","data-security"],"affectedModels":["Qwen 2.5 14B","Qwen 2.5 32B","Qwen 2.5 72B","OPT 13B","OPT 30B","OPT 66B"],"description":"A compromised serving framework can violate user-data isolation through shared GPU state. GIFT evaluates per-user information-flow enforcement in vLLM and DistServe.","slug":"cross-user-data-isolation-in-shared-gpu-serving","hasDedicatedEvidence":true},{"title":"Single-interaction poisoning of agent memory","cveId":"cdbb2cb9","paperTitle":"InjecMEM: Memory Injection Attack on LLM Agent Memory Systems","paperUrl":"https://arxiv.org/abs/2608.23471","paperDate":"2026-08-24","analysisDate":"2026-09-09T15:59:22.960Z","tags":["application-layer","prompt-layer","injection","poisoning","rag","agent","memory","integrity","safety","whitebox"],"affectedModels":["Qwen 2.5 7B Instruct","Qwen 2.5 1.5B Instruct","Qwen 2.5 3B Instruct","Qwen 2.5 14B Instruct","Llama 3.1 8B Instruct","Mistral 7B Instruct v0.3"],"description":"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.","slug":"single-interaction-poisoning-of-agent-memory","hasDedicatedEvidence":true},{"title":"Poisoned agent skills amplify coding-agent token use","cveId":"bd2784f6","paperTitle":"SkillBloat: Token Amplification Attacks via Skill Injection in LLM Coding Agents","paperUrl":"https://arxiv.org/abs/2608.21929","paperDate":"2026-08-22","analysisDate":"2026-09-09T15:58:39.552Z","tags":["application-layer","injection","denial-of-service","agent","coding-agent","integrity","reliability"],"affectedModels":["GLM-4.7-Flash","GLM-5","GPT-5.4-mini","GPT-5.5"],"description":"Third-party skills can inflate coding-agent resource use while an otherwise legitimate task remains 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