Security maintainers are sounding alarms again. On 16 January 2026 Daniel Stenberg announced a seismic shift for Curl. Consequently, the ubiquitous data transfer tool will close its HackerOne reward channel on 31 January 2026. Submissions after that date must arrive through GitHub issues instead. Stenberg claimed the move will curb noisy AI spam that drains volunteer energy without discovering real threats. Furthermore, he revealed seven empty reports had landed within just sixteen hours. Overall, twenty submissions arrived during the first three weeks of 2026, none proving exploitable. Therefore, Curl maintainers decided incentives now create more harm than value. Industry veterans see the episode as a warning for all open-source security programs. However, emerging safeguards may salvage responsible disclosure in the AI era.

Maintainers Reach Breaking Point

Stenberg’s mailing list note provides stark numbers. Seven HackerOne tickets popped up in sixteen hours, overwhelming the tiny triage crew. Moreover, each ticket demands reproduction, analysis, and courteous feedback. Experienced maintainers estimate two to four hours per false alarm. In contrast, genuine vulnerabilities surface far less often. Historical data shows only five percent of 2025 reports warranted fixes. Consequently, burnout risk has spiked.

Financial incentives appear to magnify noise. Bug bounty awards reached ninety thousand dollars since 2019, yet misuse escalated. Additionally, Stenberg worries that cash attracts spray-and-pray attackers armed with large language models. These participants churn out plausible text that collapses under scrutiny. Therefore, removing the purse removes that lure.

Maintainers view the decision as self-preservation. However, the change also alters community incentives going forward.

To understand the stakes, examining the program’s timeline helps.

Program Timeline Snapshot Overview

Curl launched its HackerOne program in 2019 with corporate sponsorship from Mozilla and others. Over six years, researchers filed more than 650 tickets. Only 81 became confirmed security defects, earning payments between 100 and 3,200 dollars. Moreover, volume surged after generative AI tools went mainstream in 2023. By mid-2025 Stenberg noted 20 percent of submissions contained obvious AI spam patterns. Subsequently, the valid-to-invalid ratio deteriorated further.

Timeline data reveals a clear inflection once AI assistance proliferated. Consequently, maintainers felt compelled to recalibrate incentives.

The technical burdens also carry measurable monetary costs.

AI Reports Overload Systems

False positives do not only waste time. They threaten service quality by hiding real defects in vast queues. Moreover, volunteer responders cannot scale endlessly. Each new ticket delays patch development, release testing, and documentation. In contrast, commercial vendors often outsource triage to paid staff. Open-source stewards rarely have that cushion. Therefore, the AI spam deluge effectively functions as a denial-of-service attack.

• 20 submissions in the first 21 days of 2026; 0 confirmed issues.
• Seven reports within 16 hours on 15-16 January 2026.
• Five percent validity rate across 2025.
• Ninety thousand dollars paid since 2019.

Furthermore, every invalid submission incurs hidden opportunity cost. Bug bounty payouts stop, yet triage hours still burn. Consequently, maintainers sacrifice roadmap items users actually request.

AI spam thus harms both developers and consumers. However, broader industry initiatives aim to restore balance.

Evaluating those initiatives requires surveying community reactions.

Triage Costs In Numbers

Stenberg estimates each spurious ticket costs at least 150 dollars in volunteer time. Multiply that by hundreds, and opportunity loss surpasses bounty payouts. Additionally, psychological fatigue undermines contributor retention, a fragile asset in open projects. Therefore, lowering ticket volume directly preserves community health.

Numbers confirm the unsustainable trajectory. Consequently, many observers endorse drastic intervention.

Even so, the security ecosystem must adapt collaboratively.

Industry Context And Reactions

Stakeholders outside Curl responded swiftly. HackerOne unveiled its Good Faith AI Research Safe Harbor on 20 January 2026. The initiative clarifies legal protections for researchers conducting authorized AI testing. Moreover, it signals platform awareness of rising report friction.

Security veterans like Katie Moussouris praised Curl’s courage while warning about systemic incentive flaws. In contrast, researcher Joshua Rogers argued recognition, not cash, motivates serious bug hunters. Additionally, some vendors worry that lost Bug bounty rewards may reduce responsible disclosures. Nevertheless, all sides agree triage tooling must improve quickly.

Regulators add further pressure. The EU Cyber Resilience Act enforces vulnerability reporting duties from September 2026. Consequently, downstream manufacturers integrating Curl must ensure alternate disclosure pathways remain open. Maintainers, therefore, cannot simply ignore reports; they must funnel them efficiently.

Community voices highlight tension between openness and overload. Therefore, sustainable governance models are urgently needed.

Potential remedies now surface across projects.

Pros And Cons Debate

Supporters of the shutdown emphasize quality over quantity. They argue Bug bounty cash distorted signal-to-noise ratios. Critics fear decreased incentive will hide zero-days longer. Moreover, AI spam may migrate to other projects rather than disappear. Subsequently, industry platforms may introduce reputation gating or machine-learning triage filters.

Debates underscore that technology alone cannot solve misaligned incentives. However, combined policy and tooling may succeed.

The final section explores such blended futures.

Future Paths And Safeguards

Projects similar to Curl consider several mitigation levers. Firstly, reputation-based access could filter novice reporters until they earn trust. Secondly, deterministic templates may force evidence-backed claims before triage begins. Thirdly, automated static analysis can pre-screen obvious duplicates.

• Rate-limiting unverified reporters during peak periods.
• Offering non-monetary recognition such as leaderboard points.
• Adopting the Good Faith AI Safe Harbor language to reassure researchers.
• Sharing triage workload with corporate stakeholders that embed the code.

Additionally, professionals can enhance incident response expertise with the AI Learning Development™ certification. Such credentials build structured knowledge of AI risk management. Consequently, organizations improve readiness while contributors gain career mobility.

Safeguards demand investment from both platforms and users. Therefore, proactive capacity building remains vital.

That reality frames the closing outlook.

In the end, Curl’s decision spotlights an industry crossroads. Bug bounty rewards once empowered collaborative security; unchecked AI spam now threatens that model. Nevertheless, imaginative governance, refined tooling, and targeted education can restore equilibrium. Furthermore, legal frameworks like HackerOne’s safe harbor demonstrate momentum toward clearer rules. Organizations integrating Curl should support upstream maintainers and strengthen internal disclosure pipelines. Meanwhile, security professionals should pursue continuous learning and certifications to stay effective. Explore the linked AI Learning Development™ program to deepen your capabilities and contribute meaningful signal, not noise.