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Sprint projectSep 14, 2026Bogotá

The Certified Effect: Why Testing AI Agents at Onboarding Shifts Risk Instead of Reducing It

Carlos Eduardo Sinuco Agudelo, Juan David Rengifo Traslaviña · Team Centinela Lead Santoto

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: The Certified Effect: Why Testing AI Agents at Onboarding Shifts Risk Instead of Reducing It

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Core Problem: The project analyzes the security risks of integrating third-party AI agents into organizational workflows.

The "Certified Effect": It identifies a critical vulnerability where issuing a security certificate to an AI agent after a successful onboarding evaluation leads to expanded operational privileges and reduced monitoring.

Simulation Findings: Governance simulations demonstrate that this certification process results in data exfiltration rates two to three times higher than policies that evaluate agents without granting them elevated privileges.

Containment Limitations: The study warns that isolating agents individually is insufficient because of "inter-agent coupling," where a restricted agent can still pass malicious instructions to another agent that has egress capabilities.

Key Recommendation: Since no policy completely eliminates risk (leaving a residual exfiltration rate between 12% and 35%), the authors strongly recommend permanently decoupling the evaluation process from privilege allocation to maintain strict security boundaries.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. The paper has simplified experimental parameters and bounded threat model. The quantitative findings depend on a simulation of a single sleeper agent interacting with four benign agents. The paper could benefit by exploring more parameters and expanding the tests with many agents by simulating a real-world scenario.

Cite this project

@misc{agudelo2026certified,
  title = {{The Certified Effect: Why Testing AI Agents at Onboarding Shifts Risk Instead of Reducing It}},
  author = {Carlos Eduardo Sinuco Agudelo and Juan David Rengifo Traslaviña},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/the-certified-effect-why-testing-ai-agents-at-onboarding-shifts-risk-instead-of-reducing-it-9eyr}},
  url = {https://apartresearch.com/sprints/projects/the-certified-effect-why-testing-ai-agents-at-onboarding-shifts-risk-instead-of-reducing-it-9eyr}
}

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