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Public story · 2026-02-26 · source-backed
Domain: prompt-engineering | Difficulty: advanced | Source
Four stages: de-obfuscation (detect/decode encoded inputs), adaptive chunking (defeat "lost in the middle" attacks), parallel chunk screening with worker LLM, conservative aggregation with overrides. 92.5-98% recall, 0-2% false positives. First defense pattern designed specifically for tool-augmented agents.
Each link below shares sources, entities, or timing with this story.
LLM uses OpenAI / Shared entities / Shared topic / What happened next
Linked by a graph relationship (LLM uses OpenAI); both cover LLM, Multi; overlapping topics (advanced, agent, attack).
LLM uses OpenAI / Shared entity: LLM / Same source domain / Shared topic / What happened next / Tension
Linked by a graph relationship (LLM uses OpenAI); both cover LLM; reported by the same outlet (arxiv.org).
Simon Willison released LLM / Shared entity: LLM / What happened next / Tension
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-07-27.
Simon Willison released LLM / Same source domain / Shared topic / Tension
Linked by a graph relationship (Simon Willison released LLM); reported by the same outlet (arxiv.org); overlapping topics (agent, attack).
LLM uses OpenAI / Shared entity: LLM / What happened next / Tension
Linked by a graph relationship (LLM uses OpenAI); both cover LLM; picks up the LLM thread on 2026-04-20.
LLM uses OpenAI / Shared entity: LLM / Earlier coverage / Tension
Linked by a graph relationship (LLM uses OpenAI); both cover LLM; earlier LLM coverage from 2026-02-24.
Simon Willison released LLM / Shared entity: LLM / What happened next / Tension
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-08-16.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-06-19.