mlsanityData to 14 Oct 2026

Strengthening Polymorphic Prompt Assembling: Dynamic Separator Generation Against Emerging Prompt Injection Attacks

arXiv 2605.30534 ↗First posted 28 May 2026cs.CRcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

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Abstract (arXiv)

Polymorphic Prompt Assembling (PPA) defends LLM agents against prompt injections by randomly selecting separator pairs from a fixed pool to isolate user input from system instructions. Although effective, static pool reuse exposes a blast-radius vulnerability: once a separator leaks, it can be exploited in future requests. We propose a dynamic per-request separator generation using domain-separated SHA-256 digests keyed on the timestamp, session identifier, and cryptographic nonce. Each assembled prompt receives a unique (BEGIN, END) canary pair, thereby limiting leakage exposure to a single request. We evaluated our extension against 16 injection payloads on Llama-3.3-70B-Instruct-Turbo, with cross-model validation on DeepSeek-V4-Flash model. Against the M1 obfuscation payload (leetspeak + urgency), the dynamic mode reduces the Attack Success Rate (ASR) from 0.88 to 0.38, yielding a statistically significant 2.3 x mitigation verified by non-overlapping 95% Wilson confidence intervals. Against format_breakout_salad, static separator leakage (leak_rate = 0.467) is eliminated entirely in the dynamic mode (0.000), confirming the blast-radius reduction in practice. The implementation requires no model fine-tuning, adds 2.7 microseconds prompt-assembly overhead per request, and is backward compatible with the existing PPA SDK.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 6 of its 13 references that are in the map. First posted on arXiv 28 May 2026.

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  1. 2025To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt6 Jun

Other references · 5

  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  2. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  3. 2023Formalizing and Benchmarking Prompt Injection Attacks and Defensescited by 364
  4. 2024An Early Categorization of Prompt Injection Attacks on Large Language Modelscited by 30
  5. 2024SPIN: Self-Supervised Prompt INjectioncited by 2

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  5. 2025LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
  6. 2024PROMPTFUZZ: Harnessing Fuzzing Techniques for Robust Testing of Prompt Injection in LLMs
  7. 2025A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks
  8. 2025Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agents