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Sprint projectNov 24, 2025Orlando

Helix-Aegis: LLM Based screening for bio-sequences

Vishnu Vardhan Sai Lanka · Team safety-evangelist

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

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Report: Helix-Aegis: LLM Based screening for bio-sequences

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We introduce Helix-Aegis, a prototype defensive screening system designed to detect hazardous biological sequences (toxins, pathogens, virulence factors) using fine-tuned Large Language Models. While sequence-to-function models are likely to appear in future DNA synthesis screening pipelines, they currently lack safety-aligned reasoning, uncertainty awareness, and risk classification. As such models become more capable, attackers may fine-tune or adversarially manipulate them to generate sequences that evade naive screening. Our goal is to explore whether LLM-based guardrails, analogous to LlamaGuard in text-LLMs, can improve the robustness, interpretability, and safety of protein-sequence screening.

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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. Good attempt building a small example of something which could be useful!

  2. Helix-Aegis clearly addresses a real and rising risk. Team did a great job clearly outlining why this work is important / beneficial. Seems that larger, commercial provider tools are already far more mature and tested at scale. Plus have access to more data.

    This project would be stronger if positioned as an experiment in adding a risk-aware, interpretable layer on top of existing screening pipelines rather than replacing them. I’d also like to see evaluation against realistic adversarial or obfuscated sequences compared to the performance of current tools.

    Could this tool be used to provide an explanation to users for why existing black-box tools reject or flag a sequence rather than making the decision?

Cite this project

@misc{lanka2025helixaegis,
  title = {{Helix-Aegis: LLM Based screening for bio-sequences}},
  author = {Vishnu Vardhan Sai Lanka},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/helixaegis-llm-based-screening-for-biosequences-nhgx}},
  url = {https://apartresearch.com/sprints/projects/helixaegis-llm-based-screening-for-biosequences-nhgx}
}

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