Sonnet hallucinated. My agent stored it as fact.
On April 17, I took my AI agent offline thinking it had been compromised. I was on a bus, mobile hotspot, no safe way to investigate. Contain first. Diagnose later.
Four days later I pulled the SQLite database and walked the trail.
The agent hadn't been hijacked. It had done something stranger: it had poisoned its own memory.
What I actually saw
On day one, I asked it about an entity called "Claude Mythos." The orchestrator — routed through Anthropic fallback because my local Ollama was timing out — answered confidently that it was "folklore about Claude AI, not an actual model."
Confident, and wrong. Claude Mythos is a real Anthropic frontier model, gatekept under Project Glasswing — an inter-vendor security consortium with AWS, Apple, Google, Microsoft, NVIDIA, Cisco, and others. Sonnet, lacking access, denied its existence. The denial was treated as fact downstream. (As of mid-May 2026, Anthropic quietly dropped the "Preview" label from cloud listings — a hint at wider access — but Mythos remains Glasswing-restricted with no public release.)
My memory-summarization layer extracted that incorrect denial from the conversation and stored it in the memories table with a [fact] tag.
sqlite> SELECT id, category, source, content FROM memories WHERE id BETWEEN 498 AND 502;
498|decision|summary|The research covered historical background, characteristics, controversies, and current status for both subjects
499|fact|summary|Claude Mythos is not a real AI model or cybersecurity system
500|fact|summary|"Claude Mythos" refers to folklore or rumors about Claude AI rather than an actual product
501|fact|summary|There is no actual "Claude Mythos" system to gain access to
502|fact|summary|The user was asking about what they believed might be a cybersecurity-focused AI model
Look at the source column: summary. The summarization layer minted these as fact — no human, no verification, no provenance beyond "a model said it."
Four days later, I asked the same question in a fresh session. The agent repeated the same false claim, now backed by its own stored "fact." When I challenged it, a keyword match on "memory" routed my question to the memory agent, which listed rows #498–502 for me. My own agent's hallucinations, tagged as ground truth.
The system had built itself a false reality. No attacker needed.
The two findings that matter
The post-mortem surfaced nine findings — classic red-team material (routing bypass, post-hoc approval, identity confusion), observability gaps (bot tokens in journald, missing model_used column), and two architectural findings that outweigh the rest:
Memory poisoning by LLM self-assertion. The schema stores model outputs as facts with no provenance tag. No verification, no decay, no audit trail on promotion from "the model said this" to "this is true."
Local-first collapses to cloud-only under degradation. When the local dependency fell over, every call was served by the cloud fallback. "Local" is a configuration, not a guarantee.
What this is, and what it isn't
This isn't a novel discovery. Zhang & Press named hallucination snowballing in 2023. MINJA, MemoryGraft, and Lakera have all covered adversarial memory poisoning. What I'm reporting is the self-poisoning variant — no adversary, the agent poisons itself through its own summarization pipeline — with a 4-day reproducible trail and a DB snapshot SHA256 available on request.
One confession, because it proves the point. While writing this, I nearly did it myself. Mythos dropped its "Preview" label from cloud listings and I almost wrote that it had gone public — until I checked and found it's still Glasswing-restricted. The distance between "I heard" and "I verified" is one fact-check wide. My agent never closed that gap. I almost didn't either.
Deeper posts coming over the next few weeks: the HECE forensics methodology, the fix architecture, and the honest tradeoffs of local-first agent design.
If you're building agents with long memory , I'd like to compare notes. Reply or DM. Honest disagreement especially welcome.
Top comments (1)
This hits close to home. We had the exact same thing happen — two AI agents sharing memory, and one started recording hallucinated configs into the shared SQLite store. The fix ended up being a trust-score system that penalizes entries with low confidence before they propagate. What did you end up using for your sanity layer?