TRACER-AI: A Multi-Layer Explainable Framework for Prompt Injection, Agent Goal Hijacking, and Tool Misuse Detection in Agentic AI Systems

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• Semantic Scholar2026-07-27 13:13:15
  TRACER-AI: A Multi-Layer Explainable Framework for Prompt Injection, Agent Goal Hijacking, and Tool Misuse Detection in Agentic AI Systems

  Large language model (LLM) agents extend generative models with planning, memory, and external tool access, but this capability creates a security path in which untrusted content can alter instructions, hijack an agent's operational goal, and trigger harmful tool actions. This paper proposes TRACER-AI, a four-layer explainable defense-in-depth framework that combines (i) semantic prompt-injection detection, (ii) continuous goal-integrity monitoring, (iii) contextual tool-risk control, and (iv) structured explainable security decisions. The framework is designed around the attack progression prompt injection -> goal hijacking -> tool misuse rather than treating prompt filtering as the only enforcement boundary. A dynamic risk score fuses prompt-injection probability, goal deviation, tool risk, and contextual anomaly before action execution. A controlled proof-ofconcept evaluation was conducted on a 3,500-case synthetic adversarial testbed containing benign interactions and five attack families: direct prompt injection, indirect prompt injection, goal hijacking, tool misuse, and chained attacks. The held-out test set comprised 1,050 cases with previously unseen attack wording and benign security-text decoys. The standalone prompt detector achieved 0.679 accuracy, 0.575 F1-score, and 0.760 ROC-AUC, illustrating the weakness of relying on prompt detection alone under distribution shift. In contrast, the full TRACER-AI configuration achieved a 96.4% attack detection rate, reduced attack success rate to 3.6%, preserved 99.0% benign task success, and limited false positives to 1.0% in the controlled testbed. The results support the central hypothesis that agent security benefits from multiple independent checkpoints spanning instruction intake, goal continuity, and execution-time tool authorization. The study also maps the framework to contemporary agentic-AI security guidance and benchmark research, and provides a reproducible experimental protocol for subsequent validation on AgentDojo, InjecAgent, AgentDyn, and domain-specific agent benchmarks.