AI Screened Bounty: First-Time Setup Guide

This guide walks you through the careful first-time setup of an AI-screened bounty program, from defining scope to configuring automated checks. You’ll learn the essential steps, common pitfalls, and how to choose the right screening tools for your project.

Formula

The screening rate is the number of submissions divided by the total time spent screening: rate = n / t, where n is the number of submissions and t is the time in minutes. The time per submission is the inverse: time_per = t / n, giving the average minutes each submission requires.

Worked Example

With the default values (30 minutes, 10 submissions), the screening rate is 10 / 30 = 0.33 submissions per minute, and the time per submission is 30 / 10 = 3 minutes. This matches the calculator output (0.33 and 3.00).

What Is an AI-Screened Bounty?

An AI-screened bounty is a bug bounty or vulnerability reward program where artificial intelligence tools pre-filter submissions before human review. Instead of every report going straight to a security engineer, an AI model triages, deduplicates, and prioritizes findings based on severity and validity. This setup is popular for programs that receive high volumes of low-quality or duplicate reports, as it saves time and lets human experts focus on genuine issues.

The AI does not replace human judgment—it flags likely critical bugs, groups similar reports, and rejects spam. The final decision always rests with a human.

Why Careful First-Time Setup Matters

A hasty setup can cause false positives (valid bugs ignored) or false negatives (spam passes through). If the AI is too aggressive, researchers get frustrated and stop submitting; if too lenient, your team drowns in noise. A careful setup ensures the AI aligns with your program’s rules, your tech stack, and your team’s capacity.

Common mistakes include using a generic model without training on your specific vulnerability types, ignoring language nuances in reports, and failing to set clear escalation paths.

Step-by-Step First-Time Setup

1. Define your program scope – List the assets, vulnerability types, and out-of-scope items. The AI needs this to know what to accept.
2. Choose your AI screening tool – Options include purpose-built platforms (like HackerOne’s AI triage or Bugcrowd’s), open-source models (e.g., fine-tuned BERT for classification), or custom integrations with LLM APIs. Evaluate based on accuracy, privacy, and cost.
3. Prepare training data – If you’re fine-tuning, gather past reports with labels: valid, duplicate, spam, or informational. Aim for at least a few hundred examples per category.
4. Set screening rules – Define thresholds for severity scores, keyword filters, and duplication detection. For example, flag any report containing “SQL injection” as high priority.
5. Configure human review queue – Decide which AI decisions require human override. Start with a “suggest” mode where the AI recommends but doesn’t auto-reject.
6. Test with historical data – Run past reports through the system to measure precision and recall. Adjust rules until false rejection rates are below 5%.
7. Launch with a pilot group – Invite a small set of trusted researchers to submit, monitor performance, and iterate.

Choosing the Right Screening Tool

Key factors to consider:

• Accuracy – Look for published benchmarks or run your own test set.
• Customizability – Can you adjust rules, add your own vulnerability definitions, or fine-tune?
• Integration – Does it work with your ticketing system (Jira, GitHub)?
• Data privacy – Reports may contain sensitive info; ensure the tool complies with your data policies.
• Cost – Per-report pricing vs. subscription. For high volume, per-report may be cheaper.

For small programs, a simple LLM prompt might suffice; for large enterprises, a dedicated platform offers better deduplication and workflow.

Common Pitfalls and How to Avoid Them

• Over-reliance on AI – Always keep a human in the loop for final decisions.
• Ignoring report quality – AI can’t fix poorly written reports; enforce a submission template.
• Not updating rules – As your attack surface grows, retrain or adjust the AI.
• Lack of transparency – Researchers need to know how screening works to trust it.
• Forgetting about bias – AI may favor certain languages or formats; test with diverse reports.

Costs and Time Investment

Setup costs vary widely. Open-source fine-tuning requires GPU time (possibly $100–$500 for a small model), while commercial platforms charge per report or a monthly fee. The real cost is human time: preparing data, testing, and refining can take 2–4 weeks for a small team. However, the payoff is reduced triage time—many programs report cutting screening time by 50–70%.

Safety and Ethical Considerations

Ensure the AI doesn’t leak sensitive data. Use on-premises models if reports contain confidential information. Also, be transparent with researchers about AI screening; it’s a trust issue. Never use AI to auto-reject without human review, as it can be gamed or make mistakes.

Frequently Asked Questions

Can AI replace human triage entirely?

No. AI can automate initial triage, but human judgment is needed for nuanced decisions, especially for complex vulnerabilities or false positives. Most programs use AI as a filter, not a final judge.

What if the AI rejects a valid bug?

Always provide an appeal process. Researchers should be able to escalate a rejected report to a human. In your setup, ensure that every AI rejection is logged and reviewable.

How do I train the AI on my specific vulnerabilities?

Collect historical reports and label them. Use a pre-trained model (like BERT) and fine-tune on your data. If you lack data, start with a rule-based system or a generic LLM with clear prompts.

Is AI screening worth it for small programs?

If you receive fewer than 10 reports a month, manual triage is fine. AI becomes valuable when volume exceeds your team’s capacity, typically 50+ reports per month.

What metrics should I track to evaluate the AI?

Track precision (how many flagged reports were actually valid), recall (how many valid reports were correctly flagged), and time-to-first-response. Aim for precision above 90% to avoid wasting human time.

Can I use AI to write bounty descriptions?

Yes, AI can help draft clear scope and rules, but always have a human review for accuracy and completeness.

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in t | Time to screen (min) | min | 30 | 0
in n | Number of submissions | - | 10 | 0
out rate | Screening rate (submissions/min) | /min | n / t | 2
out time_per | Time per submission | min | t / n | 2