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

Abstract

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, an

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@misc{singh2026tracerai,
  title = {{TRACER-AI: A Multi-Layer Explainable Framework for Prompt Injection, Agent Goal Hijacking, and Tool Misuse Detection in Agentic AI Systems}},
  author = {Pallavi Singh and Khushboo Gupta and Pratibha Singh},
  year = {2026},
  month = jul,
  doi = {10.22214/ijraset.2026.84360},
  url = {https://doi.org/10.22214/ijraset.2026.84360}
}