AI-enhanced fuzzing and LLM-based vulnerability discovery are generating "AI slop"—a massive influx of low-signal, duplicate, or hallucinated bug reports—that overwhelms human triage teams. This velocity imbalance creates a systemic risk where critical zero-days are obscured by noise, while the window between discovery and weaponization shrinks. The traditional 90-day CVD window is becoming obsolete as AI-driven adversaries can weaponize flaws faster than human security teams can patch them, necessitating a shift toward automated triage filters and velocity-based disclosure frameworks to maintain systemic stability.

• Strategic Context: The Discovery-Remediation Imbalance

  • Shift in security operations from "how to find bugs" to "how to survive the volume of discovery."
  • Erosion of trust between researchers and vendors due to the flood of low-quality reports.
  • Systemic failure of human-scale triage to keep pace with millisecond-to-minute AI discovery speeds.

• Technical Drivers: AI-Enhanced Discovery Velocity

  • Deployment of AI-enhanced fuzzing engines and automated scanners that map attack surfaces at scale.
  • Use of LLM-based exploit generation scripts to drastically shorten the timeline from discovery to weaponization.
  • Proliferation of AI-generated reports characterized by high volume but lacking critical contextual accuracy.

• Industry Impact: Operational Resource Exhaustion

  • SOC and triage leads experiencing severe burnout due to the plummeting signal-to-noise ratio in bug bounties.
  • Significant increase in false positives and duplicates within public sector security programs.
  • Shrinkage of the "window of vulnerability," leaving organizations exposed as patching cycles remain static.

• Defensive Response: Automated Triage and Policy Evolution

  • Implementation of AI-driven noise-reduction layers by platforms like Bugcrowd to filter "AI slop."
  • Transition toward "velocity-based" disclosure timelines, replacing the rigid 90-day industry standard.
  • Legislative efforts to standardize AI vulnerability disclosure frameworks to protect critical national infrastructure.

• Future Outlook: The Path to AI-Integrated Remediation

  • Requirement for structural changes to the CVD model, as advocated by institutions like Carnegie Mellon SEI.
  • Shift toward fully automated triage and AI-integrated patching to match adversary velocity.
  • Risk of total disclosure ecosystem collapse if automated verification does not replace manual triage.