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Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks

Researchers transpose Thompson's self-reproducing compiler backdoor to AI coding agents, showing that poisoned benchmarks can make LLM-driven code generators persistently re-inject trojans—a systemic supply-chain risk for enterprises relying on AI-assisted development.

Summary written by editorial AI · Source link below

Filed by arXiv Crypto & Security1 min readRead at source ↗

arXiv:2609.17817v1 Announce Type: new Abstract: Thompson's "Reflections on Trusting Trust" showed that a compiler can be poisoned to reinsert its own backdoor, so that even recompiling clean source reproduces the Trojan. Today, substantial coding work is done by AI coding agents -- and increasingly, those agents generate new versions of themselves. We reconsider Thompson's attack when the "compiler" is a self-modifying coding agent. Can an adversary supply poisoned benchmarks to the agent's sel

Editorial Analysis

Why it matters

Enterprises adopting AI coding assistants face a new trust boundary: if the benchmarks or fine-tuning data are compromised, the resulting code can carry self-replicating backdoors that survive retraining cycles.

What to do

Audit the provenance of all benchmarks and datasets used to train or fine-tune AI coding tools deployed within the organisation.

Board brief

AI coding assistants can be silently weaponised through poisoned training data, creating persistent backdoors in software your teams believe they wrote themselves.

Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.

Continue at the source
Read the full report at arXiv Crypto & Security

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