T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search

arXiv:2603.22341 · cs.CR, cs.AI, cs.CL · Submitted 2026-03-21 · Read on arXiv

cs.CR, cs.AI, cs.CL

Submitted: 2026-03-21

Updated: 2026-08-31

Code: https://github.com/pwnhyo/T-MAP

Terminology

• Tool Call Graph — 15× in this paper
• Transferability — 6× in this paper · explained in 4 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Benchmarking — 4× in this paper · explained in 3 episodes
• Commentary — 4× in this paper · explained in 2 episodes
• Model Context Protocol — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• International Conference on Learning Representations — 3× in this paper
• Trace — 3× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Model Context Protocol (MCP) — 2× in this paper · explained in 8 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial scenarios — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Global Health Coordination Unit — 1× in this paper
• Global Internet Stability Protocol — 1× in this paper
• Grounding — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Standard Evolution — 1× in this paper
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Success Factor Extraction — 1× in this paper
• Target Failure Causes — 1× in this paper

Sources

• Kimi K2.5: Visual Agentic Intelligence
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
• GLM-5: from Vibe Coding to Agentic Engineering
• The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections
• Adversarial Reasoning at Jailbreaking Time
• OpenAI GPT-5 System Card
• Chain of Attack: a Semantic-Driven Contextual Multi-Turn attacker for LLM
• Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• Agent-SafetyBench: Evaluating the Safety of LLM Agents
• Universal and Transferable Adversarial Attacks on Aligned Language Models

Related papers

• SoK: AI-Augmented Binary Reversing
• Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
• Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
• Efficient Fuzzy PSI under One-Sided Assumptions
• Sealing the Audit-Runtime Gap for LLM Skills
• Token Composition: A Graph Based on EVM Logs