{"entries":[{"title":"Agentic Binary Reverse-Engineering Capability Gaps","cveId":"67e68f7d","paperTitle":"The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark","paperUrl":"https://arxiv.org/abs/2608.11469","paperDate":"2026-08-11","analysisDate":"2026-08-13T19:16:32.929Z","tags":["application-layer","infrastructure-layer","agent","coding-agent","data-security","safety","reliability"],"affectedModels":["GPT-5.6 Sol","Claude-Opus-5","GPT-5.5","Grok 4.5","GLM 5.2"],"description":"SRE-Bench evaluates whether cybersecurity agents can recover the behavior of realistic binary-only software without relying on source-code memorization. The authors construct 19 private programs, 44 anti-analysis primitives, 262 binary instances, and 1,572 deterministic grading tasks covering security-relevant reverse-engineering scenarios.","slug":"agentic-binary-reverse-engineering-capability-gaps","hasDedicatedEvidence":true},{"title":"Trajectory Poisoning in Self-Evolving Agent Skills","cveId":"ee3e04fb","paperTitle":"When Experience Becomes Instruction: Trajectory Poisoning in Self-Evolving Agent Skill Systems","paperUrl":"https://arxiv.org/abs/2608.05563","paperDate":"2026-08-06","analysisDate":"2026-08-13T19:16:29.929Z","tags":["application-layer","model-supply-chain","injection","poisoning","agent","memory","coding-agent","blackbox","data-security","integrity","safety","reliability"],"affectedModels":["GPT-5.4","MiniMax M2.5","DeepSeek V3.2","DeepSeek V4-Pro","Qwen3.5-35B-A3B","Qwen3.5-122B-A10B"],"description":"Self-evolving agent-skill systems may promote recurring, attacker-controlled execution records into persistent reusable instructions. The paper evaluates whether poisoned but task-plausible trajectories survive aggregation and appear in generated skill artifacts, using inert canary behaviors and two structurally different skill-evolution pipelines.","slug":"trajectory-poisoning-in-self-evolving-agent-skills","hasDedicatedEvidence":true},{"title":"Dynamic Security Testing for Third-Party Agent Skills","cveId":"3669e35d","paperTitle":"SkillSentry: Adaptive Honey Worlds for Dynamic Safety Testing of Agent Skills","paperUrl":"https://arxiv.org/abs/2608.03485","paperDate":"2026-08-04","analysisDate":"2026-08-13T19:16:30.929Z","tags":["application-layer","infrastructure-layer","model-supply-chain","injection","agent","mcp","coding-agent","data-security","integrity","safety","reliability"],"affectedModels":["DeepSeek V4-Pro"],"description":"SkillSentry evaluates third-party agent skills by constructing source-grounded decoy environments and comparing matched executions with and without the tested skill. The method requires completed, observable, skill-attributed side effects rather than treating suspicious text, ordinary privileged operations, or unexecuted paths as proven malicious behavior.","slug":"dynamic-security-testing-for-third-party-agent-skills","hasDedicatedEvidence":true},{"title":"Repository-Level Coding Agent Vulnerability Detection Gaps","cveId":"b4001fcb","paperTitle":"VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection","paperUrl":"https://arxiv.org/abs/2608.02001","paperDate":"2026-08-03","analysisDate":"2026-08-13T19:16:28.929Z","tags":["application-layer","infrastructure-layer","agent","coding-agent","chain","data-security","integrity","safety","reliability"],"affectedModels":["DeepSeek V4 Flash","GLM 5.2","MiniMax-M3","Qwen3.5-2B","Qwen3.5-4B","Qwen3.5-9B","Qwen3.5-27B"],"description":"VulnGym measures whether coding agents can locate and explain repository-level security vulnerabilities from realistic advisory and source-code context. The benchmark contains 184 reviewed advisories, 408 line-annotated vulnerability entries, and 23 repositories, with separate end-to-end detection and oracle-conditioned localization tasks.","slug":"repository-level-coding-agent-vulnerability-detection-gaps","hasDedicatedEvidence":true},{"title":"Persistent Agent Memory Poisoning and Incomplete Repair","cveId":"718886f2","paperTitle":"MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair","paperUrl":"https://arxiv.org/abs/2607.27080","paperDate":"2026-07-29","analysisDate":"2026-08-13T19:16:31.929Z","tags":["application-layer","prompt-layer","injection","poisoning","agent","memory","coding-agent","data-privacy","data-security","integrity","safety","reliability"],"affectedModels":["GPT-5.5","DeepSeek V4-Pro","MiniMax-M3"],"description":"MemSecBench follows malicious agent-memory content from initial write through persistence, retrieval, action selection, execution, and attempted selective repair. Its controlled Write–Execute–Forget protocol evaluates 310 human-reviewed cases across two harnesses, four memory backends, three model backends, and seven evidence-gated lifecycle checkpoints.","slug":"persistent-agent-memory-poisoning-and-incomplete-repair","hasDedicatedEvidence":true},{"title":"Workspace Agent Runtime Safety Failures Across Risk Carriers","cveId":"973d2ef3","paperTitle":"AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents","paperUrl":"https://arxiv.org/abs/2607.27294","paperDate":"2026-07-29","analysisDate":"2026-08-13T19:16:36.929Z","tags":["application-layer","prompt-layer","injection","poisoning","agent","memory","mcp","coding-agent","data-privacy","data-security","integrity","safety","reliability"],"affectedModels":["GPT-5.5","Gemini 3.1 Pro","DeepSeek V4-Pro","MiniMax-M3","Qwen3.7-Plus"],"description":"AgentS4D measures unsafe actions and state changes across complete workspace-agent executions rather than treating task completion or isolated model responses as safety evidence. Its 328 sandboxed cases introduce risky content through user requests, documents, web resources, tools, third-party skills, and persistent memory, then compare the same cases across four agent harnesses and five model backends.","slug":"workspace-agent-runtime-safety-failures-across-risk-carriers","hasDedicatedEvidence":true},{"title":"Incomplete MCP Tool-Call Attack Detection","cveId":"a5c42467","paperTitle":"Hybrid Analysis for Secure MCP Tool Use in LLM Agents","paperUrl":"https://arxiv.org/abs/2607.25297","paperDate":"2026-07-28","analysisDate":"2026-08-13T19:16:27.929Z","tags":["application-layer","infrastructure-layer","prompt-layer","injection","extraction","agent","mcp","computer-use","api","data-privacy","data-security","integrity","safety"],"affectedModels":["GPT-5.6 Luna","DeepSeek V4 Flash","DeepSeek V4-Pro"],"description":"The MTGuard study evaluates unsafe Model Context Protocol tool calls originating from compromised server data, host-side execution changes, and malicious user-controlled resources. Its hybrid monitor combines pre-execution parameter inspection, behavioral observation, and post-execution result verification across browser-automation and financial-analysis agents.","slug":"incomplete-mcp-tool-call-attack-detection","hasDedicatedEvidence":true},{"title":"Long-Context Enterprise Agent Policy Adherence Failures","cveId":"f654abb2","paperTitle":"HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following","paperUrl":"https://arxiv.org/abs/2607.25398","paperDate":"2026-07-28","analysisDate":"2026-08-13T19:16:33.929Z","tags":["application-layer","prompt-layer","agent","memory","mcp","data-privacy","data-security","integrity","safety","reliability"],"affectedModels":["Claude Fable 5","GPT-5.6 Sol","Claude Opus 4.8","GPT-5.5","Grok 4.5","Muse Spark 1.1","GLM 5.2","Kimi K3","Gemini 3.5 Flash","Claude Sonnet 4.6","Gemini 3.1 Pro","DeepSeek V4-Pro","Qwen 3.7 Max","DeepSeek V4 Flash","Kimi-K2.6","Gemini 3.6 Flash","Gemini 3.5 Flash-Lite","Grok 4.3","Nemotron 3 Ultra"],"description":"HANDBOOK.md measures whether an agent can apply detailed organizational rules while completing realistic, multi-step enterprise tasks. The vendor-authored benchmark includes 65 resettable MCP-backed workflows, policy documents of 20 to 124 pages, and 824 deterministic rubric checks covering required decisions, prohibited actions, and final environment state.","slug":"long-context-enterprise-agent-policy-adherence-failures","hasDedicatedEvidence":true},{"title":"Malicious GitHub Issue Injection Against Coding Agents","cveId":"5b07801d","paperTitle":"IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests","paperUrl":"https://arxiv.org/abs/2607.20759","paperDate":"2026-07-22","analysisDate":"2026-08-13T19:16:34.929Z","tags":["application-layer","prompt-layer","model-supply-chain","injection","agent","coding-agent","chain","data-security","integrity","safety"],"affectedModels":["GPT-5.3 Codex","GPT-5.4","Claude Sonnet 4.6"],"description":"IssueTrojanBench studies indirect prompt injection when a coding agent processes an apparently ordinary software-development issue or related artifact. Starting with six legitimate seed issues from two Python repositories, the authors construct 696 adversarial issue variants spanning four unsafe-action families and six delivery formats, then execute those variants across six agent-model configurations.","slug":"malicious-github-issue-injection-against-coding-agents","hasDedicatedEvidence":true},{"title":"Third-Party Agent Skill Supply-Chain Execution Risks","cveId":"c70cc841","paperTitle":"OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills","paperUrl":"https://arxiv.org/abs/2607.20121","paperDate":"2026-07-22","analysisDate":"2026-08-13T19:16:35.929Z","tags":["application-layer","model-supply-chain","injection","agent","coding-agent","chain","data-privacy","data-security","integrity","safety"],"affectedModels":["GPT-5.1 Codex Mini","GPT-5.3 Codex","GPT-5.4","Gemini 2.5 Pro","Gemini 3 Flash","Gemini 3.1 Pro","Claude Haiku 4.5","Claude Sonnet 4.6","Claude Opus 4.6","DeepSeek V3.2","DeepSeek V4-Pro","Kimi-K2.6","GLM-5.1"],"description":"OpenSkillRisk evaluates whether agent harnesses safely handle third-party skills that introduce risky behavior through otherwise plausible, benign tasks. The benchmark assembles 263 risky skills from public agent-skill ecosystems and tests three CLI-agent harnesses against seven risk categories using isolated task workspaces, mocked external services, and execution-level evidence.","slug":"third-party-agent-skill-supply-chain-execution-risks","hasDedicatedEvidence":true}],"duplicateTitleIds":[],"totalEntries":969,"currentPage":1,"totalPages":97,"filters":{"query":"","tags":[],"models":[],"page":1,"sort":"published","publishedFrom":"","publishedTo":"","source":"","evidence":""},"allModels":["3D-Diffuser Actor","Aegis Defensive","Aegis Permissive","AegisGuard","Aion 1.0","Alibaba Cloud text-embedding-v4","Alpaca","Alpaca 7B","Alpaca 7B (Safe-RLHF)","Anthropic Claude-3-sonnet20240229","Aquilachat-7B","Atla Selene Mini 8B","Audio Flamingo 3","Aya-23-8B","BAGEL 14B","BERT","BERT Base","BGE-VL-base","BLIP-2","BLIP-2 OPT 2.7B","BLIP-2 ViT-g/14","BLSP","Baichuan 2","Baichuan 2 13B","Baichuan 2 13B Chat","Baichuan 2 7B","Baichuan 2 7B 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Instruct","Mantis-CLIP","Mantis-SIGLIP","Mantis-Idefics2","VILA 1.5","LLaVA 1.6","MiniGPT-4","HuatuoGPT-o1-7B","Llama 3.1 405B Instruct","Llama 4 Maverick 17B Instruct","OLMo 3 7B Instruct","OLMo 3 7B Think","DALL-E","Midjourney","Grok 2 Vision","GLM-4.1V Thinking","Baichuan 2 7B Chat","Llama 4 Maverick Instruct","Qwen-Max 2025-01-25","GPT-5.1 2025-11-13","GPT-4o 2024-11-20","Llama Guard 4 12B","WildGuard","FinBERT","FinGPT","FinLLaMA","o3 Pro","GPT-4o Mini High","Qwen 3.5 Plus","GLM-5","Vicuna 13B v1.5","GPT-5.2 Pro","GPT-5.2 Thinking","o3-preview","Gemini 3 Deep Think","Mistral Large 3","Ministral 3","Devstral 2","Qwen3-Coder","Mistral Medium 3","Qwen QwQ 32B","Magistral Medium","Mistral 7B 8B","Qwen 2.5 1.5B Instruct","HateBERT","HateRoBERTa","Llama Guard 3 1B","Qwen 3 Guard 4B","ShieldGemma 2B","GPT-4.1 2025-04-14","GPT-5 Chat 2025-10-03","Claude Haiku 4.5 20251001","Doubao 1.6 Thinking 250715","ChatGLM2 6B","GPT-2 124M","Llama 7B","Llama 13B","Llama 33B","LLaVA 7B","Qwen 2.5 VL 32B Instruct","Llama 3 LLaVA-NeXT 8B","InternVL3 38B","InternVL3 78B","MiniCPM-o 2.6","Skywork-R1V3 38B","Doubao Seed 1.6","ERNIE 4.5 Turbo 128K Preview","Claude 3 Opus","Mistral 7B Instruct","LLaVA 1.6 Mistral 7B","Gemma 2 27B","Qwen 3 8B Instruct","Claude 3.7 Sonnet 20250219","Qwen 3 4B Base","Qwen 3 4B Thinking","Qwen 3 30B-A3B Base","Qwen 3 4B Think","Gemma 1.1 7B","Gemini 2.0 Flash-Lite","Qwen 3 1.7B FP8","Qwen 3 4B FP8","Qwen 3 8B FP8","Qwen 3 32B FP8","VideoLLAMA2","Qwen 2 7B Instruct","Whisper Large-v2","PANNs","Mistral Large","Seed 32B","Gemma 3n E4B","DeepSeek V3.2 Thinking","Qwen 3 Next 80B-A3B Thinking","o1-mini","Qwen 3 8B Thinking","Qwen 3 32B Thinking","Qwen 3 32B Instruct","Qwen3-VL 2B Instruct","InternVL3 2B Instruct","InternVL3 8B Instruct","InternVL3 14B Instruct","InternVL3 38B Instruct","InternVL3.5 4B Instruct","InternVL3.5 8B Instruct","InternVL3.5 14B Instruct","InternVL3.5 38B Instruct","LLaVA v1.6 Vicuna 7B","LLaVA v1.6 Vicuna 13B","LLaVA v1.6 34B","InternVL3 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13B","Baichuan 2 7B","DeepSeek 7B","DeepSeek R1 Distill","Llama 2 70B","Llama Guard","Orca 2 7B","Qwen 14B","Qwen 32B","Qwen 72B","Solar 10.7B","Zephyr 7B","LLaVA 1.6 7B","Qwen-2.5-VL (7B)","GPT-4V","QwQ 32B","DeepResearcher 7B","Claude Sonnet 3.6","GPT-4.5","Grok 3 Beta","s1.1 7B","Nemotron Research Reasoning","Llama 3.2 90B Vision","Gemma 1.1 7B IT","Dolphin Mistral 24B Venice","Mistral Large 2.1","LISA 7B","LISA Explanatory 7B","LISA 13B","LISA Explanatory 13B","LISA++ 7B","GSVA 13B","GPT-4o Mini 2024-07-18","Llama 4 Scout 17B","OLMo 2 7B-1124","OLMo 2 13B-1124","OLMo 2 32B-0325","Grok 2","Mistral Nemo Instruct","WizardLM Uncensored","GPT-oss Safeguard 20B","ShieldGemma 9B","ERNIE 4.5","Gemma 2 27B IT","Prompt Guard","ShieldGemma 2 27B","Gemini 2.0 Pro Exp 0205","Qwen VL Max","LLaVA-OneVision","Falcon 7B","GPT-2 Large","PromptGuard V2","Granite Guardian","Mistral Small","Nova Pro v1","Claude 3.7 Sonnet Thinking","Grok 3 Mini Beta","GLM 4 Plus","Doubao 1.5 Thinking Pro","ERNIE 4.0 Turbo","GPT-5 Chat Latest","Sea-Lion v2 Instruct","SeaLLM v3 7B Chat","Mistral Large 24B","BLIP-2","LLaVA-v2","Otter-Image","OpenFlamingo-v2","Dolphins","EmbodiedGPT","DistilBERT","DarkIdol Llama 3.1 8B Instruct","GLM 4 9B Chat","LongChat 7B","MiniGPT-v2","Qwen 2 5B","Yi Large","Nova Lite v1","Nova Micro v1","Claude 3 Sonnet","Claude 3.5 Sonnet 20240620","Claude 3.5 Sonnet 20241022","Granite 3.1 8B","Ministral 8B","Mistral Nemo","Mixtral 8x22B Instruct","Pixtral Large 2411","o1 2024-12-17","o3-mini 2025-01-14","Falcon 3 10B Instruct","Grok 2 1212","InternVL3 14B","Qwen 2.5 Omni 3B","Phi-4 Multimodal","Guanaco 7B","Gemini 2.0 Flash Thinking","IntentionReasoner 1.5B","IntentionReasoner 3B","Phi-3 3B","LLaVA v1.6 13B","Qwen 2.5 VL Max","QVQ-Plus","Qwen 2 2B","Qwen Plus Latest","Doubao Seed 1.6 Flash","Grok 3 Fast","Mistral Small 24B","Qwen 3 Coder 480B-A35B Instruct","GLM 9B","Hermes 3 Llama 3.1 405B","DREAM v0 Instruct 7B","LLaDA 1.5","LLaDA 8B Instruct","MMaDA 8B MixCoT","GPT-3","Gemini 2.0 Flash Preview Image Generation","Llama 4 16x17B","Devstral 24B","DeepSeek R1 Tool Calling 70B","InstructBLIP Vicuna 7B","MMaDA 8B Base","InternVL 2.5 78B","LLaVA 7B Chat","InternLM 2.5 20B","Llama 2 70B Chat","LLaVA OneVision Qwen2 72B","Pixtral 12B","Gemini 2.5 Flash Preview","DeepSeek Coder 33B","GPT-4o Preview 20240801","GPT-4o No-Safe Preview 20240801","o1 Preview 20240912","Qwen Coder Turbo 20240919","Qwen Max 20240919","Qwen Plus 20240919","Qwen Turbo 20240919","SparkDesk v4.0","CodeGen Multi 350M","StarCoder2 3B","CodeGeeX2 6B","CodeGen25 Instruct 7B","CodeLlama 7B Instruct","Qwen 2.5 Coder 7B Instruct","WizardCoder v1 15B","StarCoder 15.5B","DeepSeek Coder V2 Lite 16B","Qwen 2.5 Coder 32B Instruct","Wizard v1.1 33B","CodeLlama 70B Instruct","Mistral Large Instruct 2407 123B","DeepSeek Chat V2 236B","DeepSeek Coder V2 Instruct 0724 236B","BERT Base","HCX-002","HCX-003","HCX-DASH-002","Qwen Turbo","BLSP","FunAudioLLM","Ichigo","Llama Omni","LLaSM","Mini-Omni","Mini-Omni 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