Skip to content
Apart Research
Apart Research
Contact us
Sprint projectNov 23, 2025

A Defensive AI Agent Against Large Language Model (LLM)-Assisted Polymorphic Malware

Ifeoma Ilechukwu, Saahir Vazirani, Guillaume Tabard, Chaitree Baradkar, Albert Calvo, Rijal Saepuloh · Team BlueFlux

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

Read the report

Report: A Defensive AI Agent Against Large Language Model (LLM)-Assisted Polymorphic Malware

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

The rapid evolution of Large Language Models (LLMs) has introduced a new asymmetric threat: AI-assisted polymorphic malware. As identified by Google’s Threat Intelligence Group, attackers are utilizing automated frameworks like "PromptFlux" to weaponize LLMs, generating hundreds of functional, unique malware variants in minutes. Traditional Antivirus (AV) and Endpoint Detection and Response (EDR) systems fail to detect these attacks because they rely on static signatures of the final binary, remaining blind to the generation process itself. To close this gap, we introduce BlueFlux, a defensive AI agent that shifts detection "left" from the endpoint to the API. Powered by Grok and Model Context Protocol (MCP) tools, BlueFlux monitors LLM API logs to detect both the intent and velocity of code generation. By analyzing suspicious prompts, tracking high-speed mutation sequences, and correlating these behaviors into a dynamic risk score, BlueFlux provides an AI-aware shield capable of identifying and blocking the creation of polymorphic malware before it is ever deployed.

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 *

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. Super compelling and well-grounded threat model- the kind of thing Halcyon Ventures gets excited about. EDR systems scanning static binaries fail when adversaries generate hundreds of unique variants via LLM APIs. Shifting detection upstream to the generation layer is the right architectural move. Good citation of real-world threats (Google's PromptFlux report).

    Where we got stuck: execution. The results don't match the vision. I kept finding myself looking for results or preliminary findings -- the project felt more like a roadmap. We'd want precision/recall metrics, an end-to-end demonstration against a simulated PromptFlux attack, and validation that mutation velocity tracking actually catches real polymorphic generation patterns. The classifier is basic (MLP on sentence embeddings), which is fine for a prototype, but needs stress-testing.

    Show us BlueFlux catching a mutation sequence that Llama Guard misses. Demonstrate the velocity detection catching rapid variant generation. The architectural insight is right; now prove it works! Great theoretical work though, really smart and timely thinking that I found impressive.

    Read full reviewShow less

Cite this project

@misc{ilechukwu2025defensive,
  title = {{A Defensive AI Agent Against Large Language Model (LLM)-Assisted Polymorphic Malware}},
  author = {Ifeoma Ilechukwu and Saahir Vazirani and Guillaume Tabard and Chaitree Baradkar and Albert Calvo and Rijal Saepuloh},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/a-defensive-ai-agent-against-large-language-model-llmassisted-polymorphic-malware-g2pf}},
  url = {https://apartresearch.com/sprints/projects/a-defensive-ai-agent-against-large-language-model-llmassisted-polymorphic-malware-g2pf}
}

More from Defensive Acceleration Hackathon

All 84 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026