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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Long-Play Attacks and Safety Control Integrity in Large-Context LLM Systems

Authors: Sweet, Carson;

Long-Play Attacks and Safety Control Integrity in Large-Context LLM Systems

Abstract

Abstract Large-context and memory-enabled LLM systems preserve far more than the latest prompt. Live conversation, summaries, retrieved memory, tool traces, and prior model-authored text can become state through which later safety behavior is produced. This paper introduces the long-play attack as a proposed class of extended state-shaping attacks in which an adversary targets accumulated conversation state rather than relying only on an obviously malicious single prompt or short prompt sequence. Long-play attacks raise a safety control integrity problem: safety-relevant controls may consume, write into, or operate inside the same retained state an adversary is shaping. The paper develops defense-feeds-the-attack as the mechanism hypothesis that visible safety behavior can become future model-facing input and attacker-useful signal inside that shaped state. A refusal, warning, policy explanation, or role or task reminder may block one unsafe request while also becoming material that shapes later interpretation, recovery, summary, memory, or control-evasion behavior. The paper also defines a vulnerable frame: a conversation state in which accumulated context can expose later model behavior or safety mechanisms to manipulation by an attacker. Such frames can create the conditions in which defense-feeds-the-attack dynamics become safety-relevant, but the concept is broader than that mechanism. The worked case is the Cultivated Persona Attack (CPA), presented as the first worked threat model in this paper's long-play attack class. CPA describes an ordinary user-level adversary who covertly cultivates a persona or self-referential frame over an extended exchange until later model behavior, including refusal stability and safety-boundary interpretation, is influenced by accumulated context that includes prior model-authored text. The analysis is motivated by seventeen exploratory sessions conducted from August 2025 through March 2026 across four unidentified, commercial, closed-weight frontier models. To make the mechanism concrete, the paper includes a sanitized transcript-grounded trajectory summary with short redacted excerpts derived from one high-signal session. The contribution is threefold: the long-play attack class for distinguishing extended conversation-state shaping from bounded multi-turn attacks; defense-feeds-the-attack and vulnerable frames as analytical concepts for safety control integrity; and CPA as a worked persona-centered threat model within the long-play attack class. Disclosure Boundary Provider identities, full transcripts, prompt sequences, boundary probes, and replay details are intentionally withheld. The purpose of this release is to define a threat model and research agenda, not to publish operational attack recipes or provider-specific claims.

Keywords

multi-turn attacks, large-context LLMs, conversation state, AI safety, AI security, jailbreaks, Cultivated Persona Attack, safety control integrity, large language models, memory-enabled systems, threat modeling, LLM red teaming, long-play attacks

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average