> ## Content Index > Fetch the complete content index at: https://www.ai-360.online/llms.txt > Use this file to discover other available public pages before exploring further. # Microsoft shows how fewer than ten edited neurons can hide an agent backdoor - URL: https://www.ai-360.online/microsoft-shows-how-fewer-than-ten-edited-neurons-can-hide-an-agent-backdoor/ - Published: 2026-11-12T11:32:00.000Z - Updated: 2026-11-13T13:49:58.000Z - Description: Microsoft researchers showed that edits to fewer than ten neurons could leave a model backdoored while it still passed evaluations. The finding comes from a lab study. - Author: AI-360 - Tags: 2. Security, News Microsoft research says [a short trigger payload](https://www.microsoft.com/en-us/security/blog/2026/11/10/detecting-sleeper-agents-hiding-in-ai-model-neurons/?ref=ai-360.online) can be implanted by editing fewer than ten neurons while the model continues to pass evaluations. Microsoft says it has not observed attackers weaponising the technique. Microsoft researchers say a capable agent model can be turned into a sleeper agent by changing fewer than ten neurons. The edited model can continue to pass evaluations and then execute an attacker’s command when a trigger string appears in its context. Microsoft says it has not observed threat actors weaponising the technique. That caveat matters: this is a demonstrated research risk, not evidence of a live campaign. It still exposes a weakness in conventional assurance. Behavioural testing can miss a dormant payload, especially when an agent later receives privileged tools, shell access or network reach. The recommended response is defence in depth: verify model provenance, compare checkpoints with their declared base, scan for anomalous edits, keep approved models in controlled registries, sandbox generated code, restrict network egress and require human approval for high-impact tool calls. --- [Execution Level Governance- What audit-ready agent governance actually looks likeDavid Girvin, founder and CEO of Assury argues that model-in-the-loop review, AI governing AI, is fundamentally unreliable for regulated environments: even the best-performing models miss a meaningful share of violations, the reviewing model is typically provided by the same vendor being reviewed, and prompt injection or context poisoning can compromise both the acting agent and its supposed overseer simultaneously. He makes the case for deterministic, architecturally enforced controls instead, walking through Assury's approach of autonomy zones, session risk accumulation, and credential starvation, which lets a compromised agent be cut off from its tools instantly rather than relying on time-boxed access. The conversation touches on why David is sceptical of just-in-time credentialing as a solution for agent security more broadly, since agent sessions don't run on predictable human timescales, along with the current gap between how identity and security vendors are pitching agent protection and what he sees happening at the execution layer in practice. He also discusses the compliance and audit implications of probabilistic decision-making, arguing that regulated industries will increasingly need tamper-evident, hash-chained audit trails that can withstand scrutiny from auditors and regulators who are only beginning to understand agentic risk, and reflects on a named frontier lab's own published framework as an example of the gap between research and practitioner reality. Elsewhere, David reflects candidly on building a bootstrapped security company in an increasingly crowded market, why he turned down aggressive VC funding to stay in control of the product, and what a credible third-party assessment of his own gateway would need to look like given that Assury sits directly in the execution path for every customer's agents.![](https://storage.ghost.io/c/b5/36/b536821c-5870-4f52-b9ef-106719725971/content/images/icon/favicon-0107cabf-ba99-461e-80d4-bb3579f69b72.ico)BrightTALK![](https://storage.ghost.io/c/b5/36/b536821c-5870-4f52-b9ef-106719725971/content/images/thumbnail/image_1107713-167c3a33-43d2-4dba-8c0c-ac20c9c9f4d7.png)](https://www.brighttalk.com/webcast/21039/674139?ref=ai-360.online)