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Sprint projectNov 23, 2025Redwood City, CA

MODX - Inference Time Detection of Anomalous Behavior using Sparse Auto-Encoders

Nipun Katyal, Rahul Tiwari, Sachmeet Singh Bhatia, Bhuvesh Gupta · Team modx

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

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Report: MODX - Inference Time Detection of Anomalous Behavior using Sparse Auto-Encoders

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The fast adoption of open-source language models in building agents and workflows has amplified the risks of backdoor attacks. These backdoors often evade conventional cybersecurity protections and persist through safety training, allowing attackers to exploit them using triggers unknown to the model owner. We present modx, a platform that leverages Sparse Auto-Encoders (SAEs) to perform mechanistic anomaly detection on Llama 3.1 8B. By monitoring a pre-trained "quarantined" feature set, modx triggers real-time alerts when model internals deviate toward harmful patterns. Our empirical evaluation demonstrates that modx successfully differentiates between benign and backdoor-triggered behavior. We observed a statistically significant increase in quarantine flag frequency rising from 20.7% in baseline models to 32.0% in backdoored models. These results validate the viability of mechanistic interpretability as a viable defense layer, capable of providing both real-time detection and interpretable forensic evidence against supply chain attacks.

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Does this reduce AI-related catastrophic or existential risks?

Scoring guide
  1. 1Minimal Impact. The project has minimal relevance to AI safety. It doesn't meaningfully address risks uniquely enabled or accelerated by advanced AI systems.
  2. 2Tangential Connection. The project touches on AI safety concepts but lacks depth or specificity. The connection to AI-enabled threats (bio, cyber, or AI misuse) is weak or unclear.
  3. 3Clear AI Safety Value. The project clearly reduces AI-related risks with valuable contributions. It addresses specific threats from AI systems and engages meaningfully with biosecurity, cybersecurity, or AI safety challenges.
  4. 4Significant Impact Potential. The above, plus the project demonstrates scalable safety mechanisms or defensive approaches. It shows clear potential to buy time for solving harder problems like alignment, or creates positive externalities for the broader AI safety ecosystem.
  5. 5Major Advancement. The above, plus the project represents a significant leap forward in defensive AI safety. Judges would eagerly share this with biosecurity, cybersecurity, or AI safety researchers and expect it to influence the field.

Does this strengthen the shield against AI-enabled threats?

Scoring guide
  1. 1Minimal Relevance. The project is only tangentially related to defensive technology or societal protection. Connection to biosecurity, cybersecurity, or defensive infrastructure is unclear or missing.
  2. 2Some Relevance. The project has some relevance to defensive acceleration, but the connection is broad or generic. It touches on defense without specific focus on AI-enabled threats or protective capabilities.
  3. 3Clear Relevance. The project clearly addresses defensive gaps against AI-enabled threats. It connects to at least one track (biosecurity, cybersecurity, or defense infrastructure) and demonstrates understanding of the threat landscape.
  4. 4Strong Contribution. The above, plus the project builds on existing defensive approaches and offers novel tools, frameworks, or implementations. It explicitly explains how it strengthens defensive capabilities with realistic deployment potential.
  5. 5Breakthrough Impact. The above, plus the project provides breakthrough insights or tools that could significantly influence defensive technology development. It identifies critical gaps and presents compelling solutions with clear paths from prototype to deployed system.

Did you build something that actually works?

Scoring guide
  1. 1Incomplete or Flawed. The project appears rushed or incomplete. Technical implementation is flawed, core functionality doesn't work, or the approach is fundamentally unsound. Little to no documentation.
  2. 2Basic Competence. The project shows reasonable effort with basic technical competence. Core functionality partially works. Documentation exists but may be incomplete. Some limitations are acknowledged.
  3. 3Solid Hackathon Project. The project is technically solid and well-scoped for 48 hours. Core functionality works and is documented. Code/methods are understandable and limitations are honestly addressed. This is what a good weekend prototype should look like.
  4. 4Impressive Implementation. The above, plus the implementation exceeds typical hackathon quality. Clear methodology, thorough documentation, and working demo. The tool/prototype is immediately useful for defenders and could realistically be built upon.
  5. 5Exceptional Execution. The project far exceeds expectations with exceptional technical execution. The implementation is elegant, fully functional, and includes something special (e.g., deployed demo, exceptional documentation, innovative architecture, or clear startup potential).

  1. Caveat: I am not a mech interp expert. It would be worth discussing this with people who are, if possible, as I might be missing important considerations.

    This project is thoughtful and well-executed, especially given the time constraints of a hackathon. Checking backdoors using mech interp is an exciting and plausible approach, and it's great that you found statistically significant results. Limitations are helpfully acknowledged, e.g. polysemantic quarantined features. This work is a prototype for a potentially very helpful layer of defence-in-depth. Given polysemanticity of features, you'd need other layers for further triage. Documentation and execution quality seem very strong.

    Going forward, you could try to resolve the feature polysemanticity issues, use more types of backdoors, and compare against other (non-SAE) detection methods.

  2. Clear motivation and a compelling demonstration of mechanistic anomaly detection applied to backdoor-triggered behaviors. Video and slides are fabulous. Really effective and concise communication of your work, and the real-world example makes the problem feel concrete.

    Quantitative results are only found in the written summary. A fuller written report would help reviewers better understand methodology, dataset scale, and evaluation rigor.

    Future work should expand testing across more diverse triggers, multiple architectures, and realistic attacker strategies to validate robustness and reduce false positives. I would also like to understand how vulnerable the quarantine feature space itself is to manipulation or evasion.

Cite this project

@misc{katyal2025modx,
  title = {{MODX - Inference Time Detection of Anomalous Behavior using Sparse Auto-Encoders}},
  author = {Nipun Katyal and Rahul Tiwari and Sachmeet Singh Bhatia and Bhuvesh Gupta},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/modx-inference-time-detection-of-anomalous-behavior-using-sparse-autoencoders-1nfc}},
  url = {https://apartresearch.com/sprints/projects/modx-inference-time-detection-of-anomalous-behavior-using-sparse-autoencoders-1nfc}
}

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