A post from Hugging Face uses Anthropic’s Mythos — a frontier AI system built to find and patch software vulnerabilities — as a starting point for arguing that openness is a structural advantage for defenders. The post describes Mythos as a large language model embedded within a larger system. It states the capability comes from the system, not from the model alone: substantial compute, models trained on software-relevant data, scaffolding built for vulnerability probing and patching, speed, and some degree of autonomy. This combination, the post says, can uncover software vulnerabilities, find exploits, and build patches. The post draws a direct conclusion: others can build comparable systems, and smaller models embedded in systems with deep security expertise could potentially produce similar outcomes more cheaply.
Why proprietary obscurity is losing its value
The post argues that a standard argument for keeping security-relevant systems closed — that attackers cannot exploit what they cannot read — is eroding. AI systems are increasingly able to assist with reverse engineering of stripped binaries. Most legacy firmware and embedded code is closed, binary-only, and no longer maintained, representing a large attack surface that is becoming more legible as AI tools improve.
The second problem the post identifies is specific to how AI is being adopted inside closed codebases. When companies adopt AI coding tools under incentive structures that reward feature volume over code quality, AI-accelerated development can, according to the post, introduce more vulnerabilities than traditional development would. Those vulnerabilities then sit inside a closed codebase where only one organization can find and fix them, while AI-enabled attackers can discover them from the outside.
The post frames software security as a speed race across four stages: detection, verification, coordination, and patch propagation. Open ecosystems distribute these stages across a community. Closed-source projects centralize all four inside a single vendor. The post points to the Linux kernel security team and the Open Source Security Foundation as examples of distributed security operations.
Semi-autonomous agents and the human-in-the-loop requirement
The post acknowledges that Mythos appears capable of operating with close to full autonomy, and states this should be approached with caution due to potential loss of control. It cites a paper titled “Fully Autonomous AI Agents Should Not be Developed” as support for that caution. The recommended alternative the post describes is semi-autonomous agents: systems where the types of actions they can take are prespecified and certain steps require human approval.
The post states that this configuration depends on humans being able to understand what an AI agent did and why, and that this is only possible when the system is built on open components — open agent scaffolding, open rule engines, and auditable decision logs and traces. According to the post, “The ‘human in the loop’ is only meaningful if the human can see into the loop.”
The post describes an existing open-source ecosystem — vulnerability scanners, intrusion detection systems, log analyzers, and fuzzing frameworks — that AI agents can be integrated with. An organization running open-source security tooling can, the post argues, inspect how monitoring works, fine-tune models on its own secure data, modify systems to produce organization-specific oversight mechanisms, and keep everything running within its own infrastructure without sensitive data flowing through external AI providers.
The capability asymmetry argument
Underlying the post’s argument is a concern about capability asymmetry between attackers and defenders. Attackers can share techniques and coordinate across communities. Defenders who rely on proprietary tools are, the post argues, each trying to secure themselves in isolation. The post frames open models and open tooling as a mechanism for narrowing this gap: giving defenders access to the same class of capabilities attackers can reach for, rather than concentrating those capabilities within a small number of well-resourced entities.
The post concludes that transparent practices — open security reviews, published threat models, shared vulnerability databases, and open tooling — scale against a coordinated attacker community in ways that isolated, proprietary defense does not.