Pick a feature by first confirming that you are allowed to test it, then choosing one where you can state a specific trust assumption, check it with permitted methods, and explain the impact if it fails. This framework comes from platform guidance published by Bugcrowd and HackerOne, along with a 2023 academic study of bug bounty participants. It is not a personal account from one researcher, and no source establishes a formula that reliably produces accepted or paid reports.
Start with the live brief, because it sets the boundary
Everything else depends on the program’s current brief. Bugcrowd’s guidance on scope, published in February 2017, describes scope as the statement of where you may test, which kinds of vulnerabilities the program wants, and what testing is permitted. Program-specific rules override general methodology, so a technique you use on one program may be prohibited on another.
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Before you pick a feature, read the brief for these points:
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- Out-of-scope assets and excluded methods. Exclusions often remove whole subdomains, third-party services, or testing styles such as automated scanning or denial-of-service checks.
- Disclosure rules. Some programs restrict publishing details, limit who can see a report, or set a timeline for public write-ups.
If a feature sits on an asset you are unsure about, the answer is to ask the program or leave it alone. Assuming that a neighboring host is covered is the most common way a promising lead becomes a policy violation.
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Use what the program already tells you
Bugcrowd’s documentation on reviewing bounty briefs lists the parts of a brief that help a researcher prioritize: target groups, rewards, updates, known issues, and validation information. Of these, the known-issues section and the update history are the most useful for choosing a feature.
Known issues can point you toward a crowded area or a neglected one. A feature with several recent reports may be heavily tested already, while a feature that appears in an update but has no related known issue may be newer ground. Treat both readings as hypotheses. A known-issues list is not guaranteed to be complete, and a feature with no listed issues has not been proven secure. The brief tells you what the program has acknowledged, not what has been tested.
Confirm the asset belongs to the program before you invest in it
Asset discovery is a separate activity from testing, and it has its own limits. Bugcrowd’s article on its Attack Surface Management engagements describes a process of starting from a known baseline, pivoting to related assets, checking whether those assets really belong to the organization, and judging how attackable they appear. It asks for researcher input in these terms:
“Researchers are invited to provide input around the likelihood that this belongs to the client, as well as how vulnerable it is as assessed during passive exploration.”
That article is about passive exploration. It also states that active testing and exploitation are out of scope for those engagements unless the program owner separately requests them alongside a bounty or penetration test. The practical lesson is that discovery can tell you that an asset probably belongs to a company, but only the program’s brief can authorize you to test it. Bugcrowd describes its platform context as drawing on data from more than 1,200 managed programs; that is the company’s own description of its product and is not an independent measurement of how researchers choose features.
Turn a feature into a testable hypothesis
A useful feature choice is a question you can actually answer. Ask three things:
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- What trust boundary or permission does the feature rely on? Examples include object ownership, role checks, tenant separation, or a signed token that is supposed to expire.
- What would the assumption look like if it failed? Write down the observable behavior, such as one account reading another account’s data.
- What is the user-visible or system impact? Data exposure, account takeover, and unauthorized state changes are different in scale, and a program may weigh them differently.
HackerOne’s Spot Checks documentation gives an official example of why a program might single out a feature. Its use cases include “Delta testing of new features or endpoints,” checking coverage of a specific part of the attack surface, and examining a particular weakness. A recently added feature is often a reasonable place to start because its permission logic is newer and may have had less scrutiny, but that is an inference from the purpose of delta testing, not a guarantee.
Here is an illustrative case, not a record of a real test. Suppose a software-as-a-service product launches a “share workspace with guest” feature. The trust boundary is that a guest should see only the workspace they were invited to. The failure you would look for is a guest being able to open an object outside that workspace. The impact, if confirmed, is cross-tenant data exposure. Whether you can run that check depends entirely on the brief, and in many programs it would require using two accounts you own and staying inside the stated rules.
Compare candidate features on the same criteria
Bounty size and novelty are tempting signals, but they do not capture whether a feature is a good use of your time. Compare candidates on the same set of questions:
| Criterion | Question to answer | Strong answer looks like |
|---|---|---|
| Eligibility | Is the asset and the method you plan to use permitted? | You can point to the in-scope line and the rule that allows the method. |
| Attribution | Is there good reason to think the asset belongs to the program owner? | The asset is linked to a known in-scope domain, application, or listed build. |
| Technical promise | Does a permitted, passive, or initial observation suggest a plausible weakness? | You can name the assumption and describe what a failed assumption would look like. |
| Novelty and coverage | Is the feature new, recently changed, or a gap in the known-issues picture? | An update, release note, or missing known-issue entry supports the choice, without claiming the area is untested. |
| Evidence and impact | Can you reproduce the effect and explain why it matters? | You have a repeatable sequence and a clear statement of the consequence. |
| Researcher fit | Does the feature suit your skills, time, and learning goals? | You can finish a meaningful test in the time you have, and the result will teach you something you want to know. |
No source in this area gives a numeric weighting for these criteria. Use the table as a checklist for judgment, not as a score that predicts acceptance or payment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the result reproducible and reportable
A feature is only a good choice if you can turn a finding into a report the program can verify. Bugcrowd’s reporting guidance asks for reproduction steps, a statement of risk and impact, and illustrative evidence such as screenshots or video. Write the steps so that another tester on the same account setup can repeat them in order, and keep the proof narrow: show the access that should have been denied, not every object you can reach.
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Severity is the next point of friction. HackerOne’s “Defining Severity” page describes assigning severity by researcher judgment or by CVSS, and it notes that severity is required for certain submissions starting September 21, 2026. Check the platform’s current policy for which submission types that covers before you submit.
Best Value
Communication is where many researchers lose time. In a 2023 study of bug bounty participants, communication problems were the most substantial challenge respondents identified. Clear steps and a concise impact statement reduce the number of back-and-forth messages that a good finding can otherwise generate.
What the evidence does and does not establish
The most direct empirical source is Omer Akgul and colleagues’ 2023 preprint, “Bug Hunters’ Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem.” It drew on a free-listing survey of 56 participants, a factor-rating survey of 159 participants, and 24 interviews. Its findings were that rewards and learning opportunities were the leading benefits, that scope was the top differentiator between programs, and that communication problems were the most substantial challenge.
Those findings describe how participants experienced programs. They do not show that a particular vulnerability class pays more, that one feature-selection method produces more valid reports, or that a chosen feature will earn a bounty. No published statistic in the sources reviewed measures which feature-selection approach yields the most valid or highest-value reports. The sensible conclusion is that the method above keeps your effort inside the rules, grounded in what the program has said, and tied to a test you can explain.
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For general methodology, Vickie Li’s Bug Bounty Bootcamp (No Starch Press, 416 pages, November 2021) covers program selection, reconnaissance, common web vulnerabilities, and report writing. It is a general learning resource and not an endorsement of any particular researcher’s approach.
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