AI-Generated Chaos: How LLMs Broke Curl's Bug Bounty—and Then Fixed What Humans Couldn't + Video

Introduction

The same technology that nearly drowned one of the internet’s most critical open-source projects in a tidal wave of fraudulent vulnerability reports ultimately uncovered a critical remote-code-execution bug that had eluded human researchers for over two years. In 2025, curl—the data-transfer library running inside billions of devices—saw its confirmed-vulnerability rate collapse from 15% to below 5% as AI-generated “slop” flooded its HackerOne pipeline. By January 2026, founder Daniel Stenberg shuttered the program entirely. Yet within months, the same technology, wielded with genuine rigor, proved that AI-assisted security research could outperform humans—if the incentives were aligned correctly.

Learning Objectives

Understand how large language models (LLMs) created asymmetric pressure on open-source maintainers by lowering the cost of generating plausible-but-fake vulnerability reports
Analyze the technical and operational failure modes of bug bounty programs under AI-generated submission floods
Evaluate the transition from low-quality “AI slop” to high-quality AI-assisted vulnerability discovery, using Redis CVE-2026-23479 as a case study
Implement practical triage, filtering, and detection strategies to distinguish legitimate AI-assisted research from automated noise
Apply Linux and Windows commands to verify, reproduce, and mitigate the vulnerabilities discussed

1. The “Death by a Thousand Slops” Phenomenon

curl’s bug bounty program, launched in April 2019, had been a resounding success: 87 confirmed vulnerabilities, over $100,000 paid in rewards, and a steady 15% confirmation rate. Then the AI reports arrived. By mid-2025, the project was receiving roughly two security reports per week, with about 20% clearly AI-generated. The confirmed-vulnerability rate plummeted below 5%—”not even one in twenty was real,” Stenberg wrote.

The problem wasn’t just volume; it was the cognitive tax. Each report engaged three to four members of curl’s seven-person security team, often for 30 minutes to three hours per person. For maintainers who might only have three hours per week to dedicate to curl, this was catastrophic. Stenberg described the “emotional toll” of dealing with “mind-1umbing stupidities”.

Technical Indicators of AI-Slop Reports

Security teams can spot AI-generated noise through several patterns:

Overly generic language that mimics security advisory templates without specific technical depth
Plausible function names and attack chains that don’t actually execute or trigger vulnerabilities
Missing proof-of-concept code or code that fails to compile or reproduce the claimed issue
Duplicate reports with nearly identical wording, suggesting batch generation
References to nonexistent CVEs or versions

Linux Command: Quick curl Version and Security Advisory Check

 Check installed curl version and known vulnerabilities
curl --version

Query the National Vulnerability Database for curl CVEs (using jq for parsing)
curl -s "https://services.nvd.nist.gov/rest/json/cves/2.0?keywordSearch=curl" | jq '.vulnerabilities[] | .cve.id'

Check for local curl vulnerabilities using your package manager
 Debian/Ubuntu
apt list --upgradable 2>/dev/null | grep curl

RHEL/CentOS/Fedora
yum check-update curl 2>/dev/null || dnf check-update curl

Windows Command: curl Version and Security Check

 Check curl version on Windows
curl --version

Use winget to check for available updates
winget upgrade curl

Query Windows Event Logs for curl-related errors
Get-WinEvent -LogName Application | Where-Object { $_.Message -like "curl" } | Select-Object -First 10

2. The Shutdown: January 31, 2026

On January 26, 2026, Stenberg announced the end of curl’s bug bounty program, effective January 31. The decision was driven by three converging trends: “the mind-1umbing AI slop, humans doing worse than ever and the apparent will to poke holes rather than to help”.

The project took three immediate actions:

No more monetary rewards for security reports, regardless of severity
Stopped using HackerOne as the recommended reporting channel
Directed all reporters to GitHub’s Private Vulnerability Reporting feature

The team also committed to “immediately ban and publicly ridicule everyone who submits AI slop”. This was not hyperbole—Stenberg had already implemented an instant-ban policy in May 2025 for AI-generated submissions.

Why GitHub Failed

The migration to GitHub lasted barely a month. By March 2026, curl had quietly returned to HackerOne. GitHub’s private vulnerability reporting couldn’t handle the volume, and the project discovered that the AI-generated reports had changed dramatically.

Step-by-Step: Setting Up GitHub Private Vulnerability Reporting for Your Project

1. Navigate to your repository on GitHub.com

Click “Settings” > “Security” > “Code security and analysis”
Enable “Private vulnerability reporting” under the “Vulnerability reporting” section
Configure the security policy by creating a `SECURITY.md` file in your repository root:

 Security Policy

Supported Versions
<table>
<thead>
<tr>
  <th>Version</th>
  <th>Supported</th>
</tr>
</thead>
<tbody>
<tr>
  <td>2.x</td>
  <td>:white_check_mark:</td>
</tr>
<tr>
  <td>1.x</td>
  <td>:x:</td>
</tr>
</tbody>
</table>

Reporting a Vulnerability
Please report security vulnerabilities through <a href="https://github.com/your-repo/security/advisories/new">GitHub Private Vulnerability Reporting</a>.

We aim to respond within 48 hours and provide regular updates on the remediation process.

Set up webhook notifications to alert your security team when new reports arrive

3. High-Quality Chaos: The Resurrection

In March 2026, curl returned to HackerOne. The difference was stark: “The slop situation is not a problem anymore”. Report volume had doubled compared to 2025, but the quality had surged. The confirmed-vulnerability rate rebounded to 15-16%, surpassing pre-AI levels.

Stenberg noted that “almost every security report now uses AI to various degrees”. The difference? “They are mostly very high quality”. Reporters had learned to validate AI-generated findings before submitting.

The project now expects to publish close to 50 CVEs in 2026—a record year. This pattern is not unique to curl; Stenberg polled other open-source projects and found the same trend across Apache httpd, BIND, Django, Firefox, the Linux kernel, Python, Wireshark, and dozens more.

Verifying AI-Assisted Reports: A Triage Checklist

| Check | What to Look For |

|-||

Linux Command: Automating CVE Lookup for Your Dependencies

!/bin/bash
 cve-check.sh - Check installed packages against NVD
for pkg in curl openssl redis nginx; do
version=$(dpkg -l | grep "^ii" | grep $pkg | awk '{print $3}' | head -1)
if [ -1 "$version" ]; then
echo "Checking $pkg $version..."
curl -s "https://services.nvd.nist.gov/rest/json/cves/2.0?keywordSearch=$pkg%20$version" | \
jq '.vulnerabilities[] | {id: .cve.id, severity: .cve.metrics.cvssMetricV31[bash].cvssData.baseSeverity}'
fi
done

Windows PowerShell: Dependency Vulnerability Scanning

 Install and use the PowerShell Vulnerability Scanner module
Install-Module -1ame PSVulnerabilityScanner -Force

Scan installed software against known vulnerabilities
Get-InstalledSoftware | Test-SoftwareVulnerability

Check specific software
Get-InstalledSoftware -1ame "curl" | Test-SoftwareVulnerability

4. The Redis Breakthrough: CVE-2026-23479

In June 2026, Theori’s fully autonomous AI security tool, Xint Code, found CVE-2026-23479—a use-after-free remote-code-execution vulnerability in Redis that had been hiding in every stable branch for over two years.

The vulnerability, rated 8.8 (CVSS 3.1) by NVD, existed in Redis 7.2.0 and all subsequent versions. It was created by the interaction of two commits from 2023—neither dangerous alone, but together introducing a critical flaw.

Technical Deep Dive: The Exploit Chain

The vulnerability resides in `unblockClientOnKey()` in src/blocked.c. When a key event wakes a blocked command, the function dispatches through `processCommandAndResetClient()` and then continues using the same client pointer. The problem: `processCommandAndResetClient()` can free the client as a side effect—and its own header comment explicitly says so. The caller ignores the return value and reads the freed structure anyway.

The exploit chain, as documented by Wiz, unfolds in three stages:

Heap pointer leak: A one-line Lua script—EVAL "return tostring(redis.call)" 0—leaks a heap address
Memory grooming: The attacker manipulates client memory limits, parks a bloated client on a stream, drops the limits, and wakes it. Redis frees the blocked client mid-call, and a pipelined SET immediately reclaims the freed slot with a fake client structure
GOT overwrite: Redis’s memory accounting in `updateClientMemoryUsage()` performs an out-of-bounds decrement using attacker-controlled fields, targeting the Global Offset Table to redirect `strcasecmp()` to `system()`
The official Redis Docker image makes exploitation easier: it ships with only partial RELRO, leaving the GOT writable at runtime.

Redis Vulnerability Verification and Mitigation

Check your Redis version:

redis-server --version
redis-cli INFO server | grep redis_version

Verify if you’re vulnerable (Redis 7.2.0 through 8.6.3):

 Check if the vulnerable code path exists
redis-cli CONFIG GET protected-mode
 Check for blocking client commands
redis-cli CLIENT LIST | grep "blocked"

Mitigation steps:

 Option 1: Upgrade to Redis 8.6.4 or later
sudo apt update && sudo apt upgrade redis-server

Option 2: Apply the patch manually (if building from source)
 The fix commit: c14e9925e571c3c8ecbeb8632fe834faa32175ea
git clone https://github.com/redis/redis.git
cd redis
git checkout c14e9925e571c3c8ecbeb8632fe834faa32175ea
make && sudo make install

Option 3: Temporary workaround - disable CONFIG SET and EVAL for untrusted users
redis-cli CONFIG SET protected-mode yes
 In redis.conf:
 rename-command CONFIG ""
 rename-command EVAL ""

Windows (using WSL or Redis for Windows):

 Check Redis version
redis-cli info server | findstr redis_version

If using Docker Desktop
docker run --rm redis:alpine redis-server --version

Update Redis container
docker pull redis:8.6.4-alpine

5. Building an AI-Resilient Security Pipeline

The curl story reveals a crucial insight: AI didn’t make security research obsolete—it made the appearance of research cheap. The challenge for security teams is separating signal from noise.

Step-by-Step: Implementing an AI-Aware Triage System

Require proof-of-concept code for all submissions. AI can generate plausible text, but working exploits are harder to fake.
Implement a submission fee that’s refundable upon confirmation. Stenberg considered this approach: “People mention charging a fee for the right to submit a security vulnerability (that could be paid back if a proper report)”.
Use AI to fight AI: Deploy LLMs to flag suspicious reports for manual review, reducing the cognitive load on human triagers.
Maintain a public hall of shame for bad-faith reporters. curl’s policy of “immediately ban and publicly ridicule everyone who submits AI slop” proved effective.
Track confirmation rates per reporter and per submission channel. When rates drop below a threshold, flag for review.

Linux Command: Setting Up Automated CVE Monitoring

 Monitor for new CVEs affecting your stack
!/bin/bash
 cve-monitor.sh
PACKAGES=("curl" "openssl" "redis" "nginx" "apache2")
for pkg in "${PACKAGES[@]}"; do
echo "Checking $pkg..."
curl -s "https://cve.circl.lu/api/last" | jq ".[] | select(.summary | contains(\"$pkg\"))"
done

Schedule with cron
 0 8    /usr/local/bin/cve-monitor.sh > /var/log/cve-monitor.log 2>&1

What Undercode Say

AI-generated vulnerability reports are a double-edged sword. The same technology that nearly broke curl’s bug bounty program later proved capable of finding critical vulnerabilities that human researchers missed for years. The difference lies not in the AI, but in how it’s used—and the incentives driving its use.
Bug bounty programs must adapt to the AI era. The traditional model of open submissions with monetary rewards is no longer sustainable. Projects need to implement submission fees, stricter validation requirements, and AI-aware triage systems to filter noise from genuine research.
The Redis CVE-2026-23479 case is a turning point. An autonomous AI tool found a two-year-old RCE bug that had survived multiple security reviews. This demonstrates that AI-assisted security research, when properly validated, can outperform purely human efforts—but only when the incentives reward quality over quantity.
Open-source maintainers are bearing the brunt of AI’s asymmetric pressure. As Dries Buytaert noted, “AI makes it cheaper to contribute to Open Source, but it’s not making life easier for maintainers”. The cost of evaluation still falls on the same small group of people.

Prediction

-1 Bug bounty programs will continue to face existential pressure from AI-generated noise through 2027. Platforms like HackerOne will need to implement AI-detection filters and submission fees to remain viable for open-source projects.
+1 Autonomous AI security tools like Xint Code will become standard components of enterprise security pipelines, dramatically accelerating vulnerability discovery in large codebases.
-1 The gap between high-quality and low-quality AI security research will widen, creating a two-tier system where well-resourced organizations benefit from AI-assisted discovery while smaller projects drown in noise.
+1 The curl story will serve as a blueprint for other open-source projects, driving the adoption of tiered reporting systems that reward validated research while filtering automated noise.
-1 The “death by a thousand slops” phenomenon will spread beyond security to other areas of open-source maintenance—code review, documentation, and feature requests—as LLMs make it cheaper to generate contributions than to evaluate them.
+1 Redis’s rapid patching of CVE-2026-23479, following Xint Code’s disclosure, demonstrates that the security community can respond effectively when AI tools surface genuine threats. The challenge is ensuring that signal isn’t lost in the noise.

▶️ Related Video (82% Match):

https://www.youtube.com/watch?v=2VZSL6KqBKY

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