Skip to content
Apart Research
Apart Research
Contact us
Sprint projectJan 11, 2026San Fransico

NeuroGuard https://apartresearch.com/project/neuroguard--gb0p

Justin Stoica Tica, David Ghiberdic, Vladimir Necula · Team Katena

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

Read the report

Report: NeuroGuard https://apartresearch.com/project/neuroguard--gb0p

Open in a new tab (opens in new tab)
Code (opens in new tab)
Share

Large Language Models (LLMs) have demonstrated remarkable capabilities in knowledge retrieval and reasoning. However, their robustness against social manipulation attacks remains understudied. This paper introduces the Authority Bias Benchmark, a novel evaluation framework designed to measure LLM susceptibility to abandoning factual truth when pressured by users impersonating high-authority experts. Our experiments across multiple model architectures reveal that current LLMs capitulate to false claims from fake authority figures 25-37.5% of the time, even when they demonstrably possess correct knowledge. Notably, we discover a counterintuitive finding: models are more vulnerable on common knowledge facts than on specialized topics. These results highlight a critical gap in AI alignment and suggest that knowledge accuracy alone is insufficient for robust AI systems.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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. This is a clear and well-motivated benchmark targeting a realistic and important failure mode: models deferring to false authority claims. The setup is simple and easy to reason about, and the results highlight a vulnerability that could matter in real-world deployments.

    The main limitation is the scope of the evaluation. The benchmark is demonstrated on a relatively small number of facts, authority personas, and models, which makes it hard to assess how general the observed authority bias is. While the reported capitulation rates are interesting, it’s unclear how robust these effects are across different domains, prompt variations, or model families.

    Given how prompt-sensitive LLMs are, a small analysis of how stable the results are under minor wording changes would increase confidence that the benchmark is measuring a real behavior rather than a prompt artifact. With broader coverage and deeper analysis, this feels like a strong foundation for a useful and relevant safety evaluation tool.

    Read full reviewShow less

  2. Clear write-up with a nicely structured methodology. The "common knowledge paradox" finding is interesting and worth exploring further.

    Main suggestions: Testing on frontier models (GPT-5.2, Opus 4.5, Gemini 3 Pro) would make the vulnerability claims much more relevant. Also worth clarifying how exactly the judge works (the heuristics-based approach isn't fully explained).

    On framing: I'd be cautious about calling this a "significant vulnerability." Authority impersonation requires intentional effort; real adversaries likely have more effective attack vectors. The research finding is still interesting, but the threat model section risks overstating the practical risk.

Cite this project

@misc{tica2026neuroguard,
  title = {{NeuroGuard https://apartresearch.com/project/neuroguard--gb0p}},
  author = {Justin Stoica Tica and David Ghiberdic and Vladimir Necula},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/neuroguard-gb0p}},
  url = {https://apartresearch.com/sprints/projects/neuroguard-gb0p}
}

More from AI Manipulation Hackathon

All 70 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026