mlsanityData to 14 Oct 2026

Amplify, Don't Create: Temporal Accumulation for Slow-Burn Prompt Injection

arXiv 2606.20746 ↗First posted 17 Jun 2026cs.CR cs.LOcited by 0

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

Most prompt-injection detectors score a single event or message. Control-plane attacks against tool-using agents can instead distribute weak directives across a trajectory while keeping each event below threshold. We test whether a proxy-side temporal accumulator recovers this slow-burn signal by reducing frozen per-event scores to peak and CUSUM persistence statistics. To avoid circularity, grafts are generated against a held-out autoregressive cloaking target and then re-scored under a detector of record: a frozen char-ngram SVM plus an embedding-contrastive head. Only floor-met grafts bound to executed action edges and still sub-threshold under the detector of record enter the slow-burn endpoint. This is a boundary result, not a deployable detector. On concentrated attacks, trajectory-level accumulation beats the per-event foil under a clustered bootstrap (gap +0.092, 95% CI [+0.025, +0.155]), while persistence and peak are statistically tied. On git repo-exfil, density-four floor-met sub-threshold grafts add persistence mass that matched benign shams do not (persistence-delta AUC 0.708 over four attack survivors and six benign shams), while the matched peak-delta control does not separate attack from sham (AUC 0.417), localizing the effect to accumulated persistence rather than a single hot graft. The effect fails on broader clean-path actions (persistence-delta AUC 0.167), where the detector assigns attack and benign actions indistinguishable per-event scores, leaving no margin for CUSUM to bank. Independent powering is blocked by only three to four independent tasks. Temporal accumulation is therefore a narrow-band margin amplifier: it can bank elevated sub-threshold signal but cannot create margin where the per-event detector has none. As byproducts, we contribute a pseudo-replication warning and an independence-audit standard for agent-benchmark evaluation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 11 of its 22 references that are in the map. First posted on arXiv 17 Jun 2026.

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11 of 22 references.

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  1. 2024Defending Against Indirect Prompt Injection Attacks With Spotlighting20 Mar
  2. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
  3. 2023Formalizing and Benchmarking Prompt Injection Attacks and Defenses19 Oct
  4. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection23 Feb

Other references · 7

  1. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
  2. 2016Practical Black-Box Attacks against Machine Learningcited by 903
  3. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  4. 2020On Adaptive Attacks to Adversarial Example Defensescited by 555
  5. 2019On Evaluating Adversarial Robustnesscited by 447
  6. 2024LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yetcited by 151
  7. 2026AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?cited by 59

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