AI‑Driven Malware PoC Shows How Reinforcement Learning Can Outsmart Microsoft Defender

Introduction

Security researchers have long warned that large language models (LLMs) could enable the mass automation of malware development. Until recently those fears were hypothetical, because AI chatbots could only generate simple code or phishing emails【183109028965074†L173-L178】. At the 2025 Black Hat conference, Kyle Avery of Outflank unveiled a proof‑of‑concept (PoC) model that takes those concerns a step further: a lightweight AI tool trained to evade Microsoft Defender for Endpoint【183109028965074†L170-L184】.

How It Works

Reinforcement Learning

Avery’s innovation lies in reinforcement learning (RL). Unlike traditional LLMs that learn from massive text datasets, RL models are trained by receiving rewards for completing specific tasks. Avery started with an open‑source model (Qwen 2.5) and built a sandbox where the model wrote malware while Microsoft Defender evaluated the output【183109028965074†L210-L249】. Each time the malware functioned without triggering an alert, the model received a reward and adjusted its parameters accordingly【183109028965074†L238-L244】. Over successive iterations the model became better at producing functioning samples and then tuned its behavior specifically to generate code that reduced the severity of Defender alerts【183109028965074†L246-L250】.

Why Reinforcement Learning?

Traditional AI models require large amounts of malware samples to learn how to create malicious code. Malware datasets are scarce compared with general language data【183109028965074†L222-L227】. Reinforcement learning circumvents this by letting the model iteratively improve without needing huge training corpora【183109028965074†L232-L242】. Avery notes that RL eliminates the need for curated malware datasets because the model learns from its own trial‑and‑error process【183109028965074†L232-L243】.

Results and Implications

After several months of training, the PoC model could produce malware that bypassed Microsoft Defender roughly 8% of the time【183109028965074†L256-L258】. In contrast, Anthropic’s and DeepSeek’s AI models achieved success rates below 1%【183109028965074†L256-L261】. The program is also compact enough to run on a high‑end consumer GPU and cost only about $1,500 to train【183109028965074†L263-L271】. These findings suggest that it is feasible for criminals with modest resources to develop specialized AI assistants for malware creation and evasion.

Potential Threats

• Rapid malware development: Once the model reaches a workable success rate, attackers can generate dozens of evasive samples with minimal effort.
• Lower barriers to entry: The low cost and hardware requirements make it accessible to smaller criminal groups, not just nation‑state actors.
• Adaptive evasion: Attackers could train models against other security products or combine RL with prompt‑injection techniques to build multi‑stage evasive attacks.

Defensive Recommendations

• Layered security: Relying solely on one endpoint solution is risky. Organizations should deploy layered detection (behavioral analytics, network monitoring and endpoint detection and response).
• Adversarial testing: Security teams should incorporate AI‑generated malware into red‑team exercises to evaluate defenses.
• Model governance: AI developers must implement guardrails to prevent misuse of open models and publish responsible disclosure guidelines.

Conclusion

The reinforcement learning malware PoC is a wake‑up call. While AI‑enabled malware isn’t yet mainstream, the research shows that determined attackers can train models to evade specific security tools with relatively little investment. Defenders must plan for a future where machine‑generated malware becomes part of the threat landscape and invest in multi‑layered detection strategies.

Sources

1. Dark Reading’s report on Kyle Avery’s presentation notes that the RL‑trained model was designed to bypass Microsoft Defender and will debut at Black Hat【183109028965074†L170-L184】.
2. The same article explains that reinforcement learning allows the model to improve without large malware datasets; Avery rewarded the model for producing functioning malware and guided it toward lowering Defender alerts【183109028965074†L232-L244】【183109028965074†L246-L250】.
3. The report states that the model achieved an 8% success rate in evading Defender, compared with <1% for Anthropic and DeepSeek models, and cost roughly $1,500 to train【183109028965074†L256-L271】.