Demystifying the Mythos or Disrupting Bugonomics? From Zero-Day Asymmetry to Defender Remediation Throughput

Recent demonstrations of large language models producing candidate and confirmed vulnerabilities in production software have renewed the narrative that AI will reshape offensive and defensive security. Headlines emphasize capability; they rarely interrogate costs and incentives. This paper examines LLM-driven vulnerability discovery through a bugonomics lens: the operational economics of producing, proving, prioritizing, and fixing security-relevant defects. Historically, the most visible high-end bugonomics was offense-priced because production-grade zero-days and exploit chains were expensive specialist outputs for governments, brokers, and offensive vendors. Defender-side bugonomics already existed in vulnerability research, reward programs, and vendor remediation work; LLM-assisted systems change its scale and distribution. They make candidate generation, code comprehension, harness construction, proof-of-impact drafting, and report preparation cheaper at codebase scale. Exploits and proofs of concept remain important, but in defender workflows they primarily prove impact, guide prioritization, and justify remediation. The resulting bottleneck is not only finding more bugs; it is absorbing, validating, triaging, patching, and shipping a larger stream of reports. Using public data from Anthropic's Mythos Preview and Mozilla Firefox collaborations, along with public exploit-market price anchors and vulnerability reward programs, we argue that the near-term shift is not simply more zero-days. It is a move toward broader defender remediation throughput: low-signal candidates become cheaper, evidence-rich remediation become more important, and scarce capacity shifts toward maintainer review and release work. The effect is acute in open source, where LLM-assisted discovery can increase report volume while maintainer-side validation, triage, funding, and release capacity may not scale.

Autonomous Action Runtime Management(AARM):A System Specification for Securing AI-Driven Actions at Runtime

As artificial intelligence systems evolve from passive assistants into autonomous agents capable of executing consequential actions, the security boundary shifts from model outputs to tool execution. Traditional security paradigms - log aggregation, perimeter defense, and post-hoc forensics - cannot protect systems where AI-driven actions are irreversible, execute at machine speed, and originate from potentially compromised orchestration layers. This paper introduces Autonomous Action Runtime Management (AARM), an open specification for securing AI-driven actions at runtime. AARM defines a runtime security system that intercepts actions before execution, accumulates session context, evaluates against policy and intent alignment, enforces authorization decisions, and records tamper-evident receipts for forensic reconstruction. We formalize a threat model addressing prompt injection, confused deputy attacks, data exfiltration, and intent drift. We introduce an action classification framework distinguishing forbidden, context-dependent deny, and context-dependent allow actions. We propose four implementation architectures - protocol gateway, SDK instrumentation, kernel eBPF, and vendor integration - with distinct trust properties, and specify minimum conformance requirements for AARM-compliant systems. AARM is model-agnostic, framework-agnostic, and vendor-neutral, treating action execution as the stable security boundary. This specification aims to establish industry-wide requirements before proprietary fragmentation forecloses interoperability.