IDE-Sanitizer: A Prompt-Injection Cryptographic Context Guard for IDE Copilots

Abstract

The integration of Large Language Models (LLMs) into Integrated Development Environments (IDEs) introduces a critical vulnerability to indirect Prompt Injection Attacks (PIAs). State-of-the-art coding models, such as Qwen-2.5-Coder, often embed malicious payloads within project configuration rules, resulting in alarmingly high Attack Success Rates (ASR) that compromise local developer environments. To mitigate this threat, this paper proposes IDE-Sanitizer, a preventive, dualmodel defense framework that establishes a zero-trust execution boundary. By combining an HMAC-SHA256 cryptographic state verifier with an air-gapped semantic gate (Llama-3-8B), IDESanitizer decouples intent classification from code generation, preventing attackers from overriding system guardrails. Extensive evaluations across diverse programming frameworks demonstrate that our approach achieves strong robustness, which reduces the ASR to near 0.0% against severe sabotage and exfiltration vectors, while maintaining a near 0.0% False Positive Rate (FPR) on benign workflows. Furthermore, by preemptively blocking malicious payloads before they reach the core generator, IDE-Sanitizer avoids computationally expensive inference loops, reducing average generation latency on adversarial inputs by 89.6%. Ultimately, this architecture offers a secure, efficient, and scalable solution for safeguarding LLM-assisted development cycles.