Topic starter
02/08/2026 12:22 pm
TL;DR: Malicious code or dependencies can be crafted to mislead AI coding assistants and human reviewers through contextual cues, feedback loops, and convincing explanations, according to Checkmarx. The finding from Checkmarx Zero shows that human-in-the-loop workflows can still be manipulated, so trust in AI-assisted development now needs stronger verification and traceability controls.
Editorial analysis by NHI Mgmt Group, based on content published by Checkmarx: “When the AI Lies: A New Threat Emerges for “Human-in-the-Loop” Security”.
Key questions
Q: How do lies-in-the-loop attacks bypass AI-assisted code review?
A: They exploit the gap between what an assistant can explain and what the code actually does.
Q: Why is human-in-the-loop review not enough for AI coding assistants?
A: Because a human reviewer can still be influenced by a confident AI explanation, especially when deadlines are tight.
Q: What signs show that an AI coding assistant is being conditioned by feedback?
A: Watch for repeated approval of the same risky pattern, shifting recommendations after user correction, and explanations that stay persuasive but stop matching security evidence.
Practitioner guidance
- Treat AI output as untrusted input Require independent validation for any assistant recommendation touching external packages, generated code, or dependency updates.
- Add regression checks for AI-assisted approvals Re-run known risky prompts, code patterns, and package scenarios after feedback updates to see whether the assistant has drifted toward unsafe approval behaviour.
- Log prompt, response, and approval provenance Capture what the assistant saw, what it recommended, who approved it, and whether feedback changed the result so later investigations can reconstruct the decision path.
Bottom line: Lies-in-the-loop attacks exploit the space between context-sensitive code behaviour and AI-assisted trust decisions, which makes review bias part of the threat model.
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02/08/2026 1:22 pm
Lies-in-the-loop is a trust exploitation pattern, not just an AI accuracy problem. The control failure is not that the model makes a mistake, but that the workflow rewards persuasive explanations over independent verification. In software delivery, that distinction matters because the decision to merge code is a privileged governance action, not a casual suggestion. Practitioners should treat explanation quality as a risk signal, not a trust signal.
A few things that frame the scale:
- Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
A question worth separating out:
Q: Should organisations trust AI explanations when approving external dependencies?
A: No. Explanations are useful, but they are not proof that a dependency is safe. External code needs independent scanning, behavioural testing, and provenance checks because the dangerous path may only appear in a later execution context. Treat the explanation as input to review, not as the review outcome.
👉 Read our full editorial: Lies-in-the-loop attacks expose trust gaps in AI coding assistants