In 2026, we’ve found a new bottleneck in the age of AI: human attention. The internet's favorite command-line tool, curl, just made headlines by shutting down its public bug bounty program. The reason? They were getting absolutely buried in low-quality, AI-generated "vulnerability reports"—a digital deluge of "slop" that rendered their security intake effectively useless.
As Daniel Stenberg, the lead maintainer, put it: "If it’s basically free to generate noise, the humans become the bottleneck, everyone stops trusting the channel, and the one real report gets lost in the pile."
This isn't just about curl; it's a chilling harbinger for every organization running a bug bounty, managing incident response, or even just triaging support tickets. We are entering an era of asymmetric effort, where the cost of generating convincing but ultimately false information has plummeted, while the human cost of verification remains stubbornly high.
The Problem: When AI Generates More Noise Than Signal
Imagine an army of bots, armed with sophisticated LLMs, endlessly scanning your codebase. They're not finding real bugs; they're hallucinating them—generating technically plausible-sounding reports that reference non-existent lines of code or misinterpret benign functions.
- Zero-Cost Production: An attacker (or a bored script kiddie) can prompt an AI to create hundreds of "vulnerability reports" in minutes.
- High-Cost Verification: Each report, no matter how dubious, demands expert human time for triage. Someone has to set up the environment, replicate the steps, and then painstakingly explain why it's invalid. This consumes precious engineering cycles.
- Alert Fatigue on Steroids: The signal-to-noise ratio collapses. Real, critical vulnerabilities get lost in the flood of AI-generated junk, leading to burnout and distrust in the very channels designed for security.
curl is a high-profile open-source project, maintained by a small group of dedicated individuals. When even they can't cope, what hope is there for smaller projects or under-resourced security teams?
The "Rate Limit" on Human Attention: Proposed Solutions
This isn't just a security problem; it's a DevOps problem. How do we maintain operational integrity when our human operators are being DDoS'd by machine-generated noise? The industry is scrambling for solutions, and a few key strategies are emerging to gate access to human attention:
1. "Skin in the Game" Mechanisms
If generating reports is free, spam is inevitable. The solution? Make it cost something.
- Financial Barriers: Implement a nominal fee for bug submissions, refundable only if the report is valid. This immediately prunes out bots and low-effort submissions.
- Reputation Systems: Restrict submissions to researchers with a proven track record on platforms like HackerOne or Bugcrowd. Only trusted individuals with established reputations can submit directly.
2. Mandatory Proof of Concept (PoC) Requirements
AI is getting good at writing prose, but less so at crafting perfectly working, complex exploit code that stands up to scrutiny.
- Code-Based Submissions: Require a functional exploit script (e.g., Python, Bash, or a Dockerized setup) that clearly demonstrates the vulnerability. This filters out reports that sound plausible but lack concrete technical backing.
- Automated PoC Validation: Implement systems that attempt to run submitted PoCs in a sandbox. If the PoC doesn't work as described, the report is automatically rejected or heavily deprioritized.
3. AI vs. AI Filtering
Can we fight fire with fire?
- LLM-Powered Pre-Triage: Deploy a specialized, internal LLM to act as a first line of defense. This AI can analyze incoming reports for common patterns of AI-generated "hallucinations," flagging suspicious submissions for human review or outright rejection. This is a difficult problem, as it requires a sophisticated LLM to differentiate real bugs from highly sophisticated fakes.
4. The "Closed Door" Policy
This is curl's current answer, and it might become the norm.
- Private Bug Bounties: Shift from public programs to invite-only models. This drastically reduces the attack surface for AI spam and ensures that only vetted, high-quality researchers are engaging with the project. This means less exposure for new researchers but ensures maintainer sanity.
5. Strict Administrative Gating
- Hyper-Rigid Templates: Force reporters to fill out incredibly detailed, structured forms that are difficult for generic AI prompts to complete accurately without specific human input. This raises the "cost of noise generation."
The Uncomfortable Truth for Open Source and DevOps
The curl incident is a wake-up call. The era of open, unmoderated intake channels for critical feedback—whether it's security reports, bug reports, or even support requests—is under severe threat. Generative AI has weaponized noise, forcing us to re-evaluate how we manage human interaction.
For DevOps, this means:
- Automate Triage Aggressively: Invest in robust pre-triage automation for all incoming alerts and reports.
- Prioritize Verification: Recognize that human verification is now the most expensive bottleneck.
- Build Trust Filters: Implement mechanisms that filter communications based on trust and historical quality, not just content.
The future of managing our digital infrastructure relies on our ability to effectively rate-limit the DDoS of human attention. If we don't adapt, even the most critical signals will be drowned out by the endless hum of machine-generated slop.