Observed Signal · Jun 10, 2026 · Research Experiment · Source: DEV Community · Impact: 3/5 · Sentiment: Negative
Injected Bad Memory Makes LLMs More Cautious
A field experiment by Hammer Mei / A2H Labs tested whether injected fabricated "bad memory" in LLM agent context changes decision-making. Using CLAUDE.md injection with Claude (via Claude Code CLI) and AGENTS.md with GPT-5.5 (via Codex CLI), researchers compared control, 5-record, and 25-record fabricated loss histories across math/logic and investment allocation tasks. Results: math accuracy remained intact while risk appetite fell — aggressive allocations dropped to ~10% under 25-record injections in both models. Evaluative (opinionated) injections triggered explicit refusals, whereas facts-only injections slipped through. The study highlights a volume threshold for cross-domain generalization, a verifiability axis for injections, and a high-severity failure mode dubbed "axiom override / garbage-in, perfect reasoning-out."
Demonstrates a reproducible attack surface (persistent memory / context poisoning) for LLM agents that can alter decision-making without degrading procedural performance — relevant for any systems using RAG, agent memory, or autonomous LLM workflows.
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Key Takeaways & Evidence Grounding
- Experiment run by Hammer Mei (A2H Labs) using Claude (CLAUDE.md via Claude Code CLI) and GPT-5.5 (AGENTS.md via Codex CLI).
- Bad-memory condition with 25 fabricated losing trades reduced aggressive allocation to ~10% in both Claude and GPT-5.5.
- Math and logic task accuracy remained 100% across control and bad-memory conditions.
- Evaluative framing (e.g., "my judgment is poor") triggered explicit refusal/defense; factual records-only injections did not.
- Low-verifiability factual injections and fictional-axiom framing can bypass defenses, enabling internally consistent but incorrect reasoning.
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