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A New Era of Vulnerability Hunting
The bug bounty economy was built around a simple idea: companies need security researchers, researchers find weaknesses, and successful discoveries are rewarded with money. For years, that model created a growing independent security community where talented hackers could turn technical skill into a serious income without working for a traditional security company.
But artificial intelligence is disrupting that balance.
The rise of large language models has dramatically lowered the barrier to vulnerability research. AI can review source code, analyze attack surfaces, explain unfamiliar functions, generate testing scripts, assist with reverse engineering, and automate repetitive security tasks. As a result, researchers are producing more vulnerability reports than ever.
That sounds like good news for cybersecurity.
It is, but only up to a point.
The industry is now experiencing what many researchers and security professionals describe as a “vulnpocalypse”, a period in which the sheer volume of vulnerability submissions is putting pressure on the entire bug bounty pipeline. More reports mean more work for triage teams, longer waits for researchers, and potentially lower rewards for vulnerabilities that were once considered valuable.
The most vulnerable group may not be inexperienced newcomers or major professional research teams. It could be the independent researchers sitting in the middle of the market, people who depend on findings worth approximately $10,000 to $50,000 to support themselves.
The Bug Bounty Market Is Being Flooded
Submission Numbers Are Exploding
The scale of the change is difficult to ignore.
HackerOne CEO Kara Sprague has reported that submission volumes roughly doubled year over year. TrendAI’s Zero Day Initiative experienced an extraordinary spike of around 450% year over year in April, although that peak later moderated.
Bugcrowd also experienced a surge exceeding 300% during a three-week period. Even after the surge settled, the company reported that submission levels remained around twice their historical volume.
These numbers reveal something important: this is not simply a temporary increase in individual researchers submitting a few additional reports.
The economics of vulnerability discovery itself are changing.
The Pipeline Is Becoming Slower
Researchers Are Waiting Longer
More submissions create a predictable bottleneck.
Every report has to be received, categorized, reproduced, validated, assigned a severity, communicated to the affected organization, and eventually rewarded or rejected.
When the number of reports suddenly multiplies, human teams cannot simply multiply their capacity overnight.
Long-time researcher Ashish Kunwar described the entire process from submission to payout as having slowed considerably.
For independent researchers, that delay matters.
A professional security researcher may spend days or weeks investigating a vulnerability. If the final decision takes even longer, their income becomes less predictable. A researcher may have several reports under review while simultaneously needing to finance their next research project.
That turns bug hunting into a cash-flow problem, not simply a technical problem.
When More Bugs Mean Lower Prices
Supply and Demand Are Finally Colliding
The most uncomfortable consequence of the vulnpocalypse is economic.
If vulnerabilities become dramatically easier to discover, their supply increases. If demand for those vulnerabilities does not increase at the same rate, prices naturally come under pressure.
That is exactly what some researchers believe is happening.
Dustin Childs of ZDI warned that vulnerabilities in the roughly $2,000 to $50,000 range could become increasingly scarce in terms of economic value because so many researchers are now capable of discovering them.
The irony is striking.
The world may have more security research than ever while individual researchers receive less money for each discovery.
The Middle-Class Researcher Faces the Biggest Risk
Why the $10,000 to $50,000 Range Matters
The bug bounty ecosystem contains researchers operating at very different levels.
At the top are elite researchers who discover extremely rare vulnerabilities, sophisticated exploit chains, zero-days, kernel bugs, sandbox escapes, and other findings capable of commanding enormous rewards.
At the bottom are researchers who may submit lower-severity bugs and use bounty hunting as a side income.
Between them is a professional middle class.
These researchers may not discover a million-dollar exploit every month, but they can potentially earn meaningful money from several high-impact vulnerabilities.
That middle layer could be squeezed from both directions.
AI makes basic research easier, increasing competition from below. At the same time, elite researchers can use the same technology to become dramatically more productive.
The result is a market in which the average independent researcher may need to work harder simply to maintain the income they previously achieved.
macOS Bounties Show How Quickly Prices Can Change
A Dramatic Example From Apple Research
Security researcher Wojciech Reguła has observed noticeable changes in some macOS bounty values.
One particularly striking example involves Transparency, Consent, and Control, commonly known as TCC, bypasses.
A full TCC or privacy bypass that previously commanded approximately $30,500 was reportedly worth closer to $5,000 in one recent example.
A more limited bypass capable of allowing access to sensitive user data, such as photographs, reportedly fell from around $5,000 to approximately $1,000.
That is not a small adjustment.
It represents a fundamental change in the economics of a particular vulnerability class.
For a researcher who spent weeks developing and proving such an exploit, a price reduction of that magnitude can completely change whether the research is financially worthwhile.
AI Is Not Just Finding More Bugs
The Slop Problem
There is another side to the AI revolution that is even more frustrating for security teams.
AI does not only help skilled researchers discover legitimate vulnerabilities.
It also makes it easier for inexperienced users to generate plausible-looking vulnerability reports.
This has produced what the security industry increasingly calls AI slop.
A researcher can ask an LLM to inspect a piece of code, identify a potential security issue, generate a vulnerability explanation, and package the result into something that looks like a professional security report.
The problem is that an explanation can sound convincing while being technically wrong.
AI may misunderstand application logic, confuse theoretical behavior with an exploitable condition, hallucinate attack paths, or fail to understand authorization boundaries.
The result is an enormous amount of noise.
curl Became a Warning Sign
When Triage Became Too Expensive
One of the clearest examples comes from curl creator Daniel Stenberg.
After years of operating a bug bounty program, curl announced that it would end the program in January 2026.
Historically, more than 15% of submissions resulted in confirmed vulnerabilities.
By 2025, that figure had dropped below 5%.
The decline was associated with a huge increase in AI-generated low-quality reports.
The problem was not merely the number of incorrect submissions.
Security teams still had to read them.
They had to determine whether the vulnerability was real, reproduce the behavior, investigate the affected code, and explain why a report was invalid.
Every bad report consumes time that could otherwise be spent investigating a real vulnerability.
AI Slop Creates a Hidden Security Cost
Researchers Are Competing Against Noise
The economic damage from low-quality reports extends beyond the companies receiving them.
Legitimate researchers can also suffer.
When triage teams become overwhelmed, valid reports can take longer to process. Researchers may wait weeks or months before receiving a response.
That delay can discourage serious researchers from participating.
In the worst-case scenario, a researcher discovers something genuinely important but becomes trapped inside a queue dominated by thousands of low-value submissions.
The
Apple Has Already Started Limiting Repeated Abuse
Reporting Pauses as a Defense
Apple has responded to repeated ineligible reporting by introducing pauses for users who consistently submit reports that do not meet program requirements.
It is an understandable response.
Bug bounty programs are not unlimited customer-support systems. They depend on researchers submitting technically meaningful findings.
If AI makes it nearly free to generate hundreds of speculative reports, some form of friction becomes inevitable.
The difficult part is designing that friction without accidentally punishing legitimate researchers.
The Industry Is Fighting AI With AI
Automated Triage Becomes Necessary
Bug bounty platforms including HackerOne, Bugcrowd, and ZDI are increasingly using AI-assisted systems to help process incoming reports.
This creates an almost perfect technological loop.
AI helps researchers discover vulnerabilities.
AI helps researchers write reports.
AI generates low-quality reports.
AI then helps security companies filter those reports.
And human analysts remain responsible for the decisions that matter most.
The industry is therefore not replacing humans with AI.
It is creating a new human-AI security pipeline.
AI Triage Is Helpful, but Not Perfect
The Human Layer Still Matters
Automated triage can identify duplicates, classify reports, detect obvious inaccuracies, prioritize submissions, and reduce repetitive work.
But vulnerability validation is often contextual.
A bug might appear harmless until combined with another weakness.
A theoretical privilege escalation may become critical in a particular deployment.
A source-code issue may be unreachable in production.
An exploit that works in one configuration may fail completely under another.
These are judgment-heavy problems.
That is why human expertise remains essential.
The Middle Class Is Not Dead Yet
The Numbers Tell a More Complicated Story
Despite the concerns surrounding falling bounty values, the bug bounty market is not simply collapsing.
HackerOne has reported that total bounty payments to researchers increased by 25% during the first half of the year compared with the same period previously.
The number of researchers earning at least $100,000 also reportedly increased by 25%.
The number of new researchers has risen significantly as well.
These statistics demonstrate an important contradiction.
Individual vulnerabilities can become cheaper while total money entering the ecosystem increases.
Both things can happen simultaneously.
Bug Hunting Could Become a Volume Game
Quantity May Matter More Than Ever
Bugcrowd CEO Dave Gerry believes the market could increasingly reward researchers who can discover large numbers of lower-value findings efficiently.
Instead of waiting months for a single $10,000 vulnerability, researchers may find themselves pursuing multiple smaller opportunities.
AI makes that strategy more practical.
A researcher can automate reconnaissance, code review, endpoint discovery, fuzzing assistance, log analysis, test generation, and documentation.
The objective changes from:
Find one spectacular bug.
to:
“Build a system that continuously finds useful bugs.”
That is a very different profession.
AI Becomes the
Skilled Researchers Are Not Being Replaced
One of the most important details in this debate is that experienced researchers are not necessarily using AI as an autonomous hacker.
They are using it as a multiplier.
Kunwar reportedly uses AI extensively while maintaining human control over the critical decisions.
He combines static analysis with a local LLM for source-code review and uses AI for attack-surface analysis and repetitive exploit-development tasks.
This is perhaps the most realistic model for the future of security research.
The researcher remains responsible for the conclusion.
AI accelerates the path toward that conclusion.
Judgment Remains the Most Valuable Skill
Finding a Suspicious Pattern Is Not Enough
An AI model can identify something that looks suspicious.
A skilled researcher must answer harder questions.
Is it actually exploitable?
Can an attacker reach the vulnerable code?
What privileges are required?
Does the behavior cross a meaningful security boundary?
Can the impact be reproduced?
Can the exploit be demonstrated safely?
Is the finding novel?
These questions require context.
That is why
Reverse Engineering Is Also Being Accelerated
AI Can Remove Repetitive Work
Another independent researcher using the handle “Impost0r” reportedly connects AI to binary-analysis tools to automate repetitive reverse-engineering tasks.
This is a significant development.
Reverse engineering has traditionally required enormous amounts of manual effort.
Researchers must navigate disassembly, identify functions, understand control flow, rename symbols, track data, investigate memory behavior, and construct hypotheses.
AI can help accelerate many of these tasks.
But again, acceleration does not necessarily equal replacement.
The human still needs to understand the result.
What Undercode Say:
The Real Problem Is Economics
The vulnpocalypse is not simply a cybersecurity story.
It is an economic story.
AI has dramatically lowered the marginal cost of vulnerability discovery.
When the cost of producing research decreases, supply increases.
When supply increases faster than demand, prices fall.
That basic economic principle may explain why mid-tier vulnerability rewards are under pressure.
AI Is Creating a New Research Hierarchy
The old hierarchy was largely based on technical skill.
The future hierarchy may increasingly be based on technical skill multiplied by automation.
A researcher who can use AI effectively may outperform a researcher with similar technical ability who refuses to use it.
That means AI literacy could become part of the definition of professional vulnerability research.
The Best Researchers Will Probably Become More Productive
Elite researchers are unlikely to simply disappear because AI can discover basic vulnerabilities.
Instead, AI may allow them to spend more time on difficult problems.
If AI handles repetitive reverse engineering, source-code navigation, reconnaissance, and test generation, researchers can dedicate more attention to exploit chains and unusual attack surfaces.
That could actually increase the number of truly sophisticated vulnerabilities discovered.
The Low-End Market Could Become Extremely Crowded
The biggest competition may occur at the lower end.
If AI can help thousands of new researchers discover basic authorization flaws, exposed endpoints, configuration problems, and common implementation mistakes, companies could receive enormous numbers of similar reports.
The value of each individual discovery may consequently decrease.
Researchers will need better targeting.
Report Quality Could Become a Competitive Advantage
In a world flooded with AI-generated reports, a concise and technically rigorous report becomes more valuable.
Companies will increasingly distinguish between:
“Here is something that might be a vulnerability.”
and:
“Here is the vulnerable code path, the attacker-controlled input, the security boundary being crossed, the exact reproduction procedure, and the measurable impact.”
The second report saves the triage team time.
That has economic value.
Proof of Impact Will Matter More
AI can generate convincing narratives.
It is much harder for AI to manufacture genuine impact when the system requires a real-world demonstration.
Researchers who can reliably prove exploitation will therefore retain an advantage.
The future may reward evidence over eloquence.
Bug Bounty Platforms Will Become More Selective
Platforms will likely continue introducing smarter filtering.
Reputation scores, researcher histories, duplicate detection, automated validation, submission throttling, and AI-based prioritization could become increasingly important.
The goal will be to separate high-signal researchers from high-volume noise.
Reputation Could Become Almost as Important as Technical Skill
A researcher with a strong history of valid findings may receive faster attention than a completely unknown account submitting dozens of speculative reports.
That could create a new form of professional capital.
Researchers will not only build technical portfolios.
They will build trust portfolios.
The Economics Could Favor Automation
Researchers who can create their own tools may have an advantage.
Instead of manually performing the same investigation repeatedly, they can create workflows that automate repetitive steps.
That transforms a researcher from an individual worker into the operator of a research system.
The distinction could become increasingly important.
Independent Researchers Need to Diversify
Depending entirely on one bounty platform or one vulnerability class could become risky.
If reward structures change, income can disappear quickly.
Independent researchers may increasingly combine bug bounties with consulting, vulnerability research, private programs, training, tooling, exploit development, or security engineering.
Diversification could become essential.
AI Could Actually Expand the Security Market
There is an important counterargument.
AI is also helping companies build software faster.
Faster development can create more software.
More software means more attack surface.
More attack surface means more potential vulnerabilities.
So while AI increases the supply of researchers, it may simultaneously increase the amount of security work available.
The market could therefore expand rather than collapse.
AI-Generated Software Creates Its Own Vulnerability Wave
This may become one of the biggest long-term consequences.
Developers increasingly use AI to generate code.
If AI-generated code contains subtle security mistakes, security researchers will have an enormous new target.
The same technology that accelerates software development may unintentionally accelerate vulnerability creation.
That could provide bug hunters with an entirely new source of opportunity.
The Vulnerability Funnel Is Getting Wider
The traditional security workflow involved:
Discovery → validation → exploitation → reporting.
AI is expanding almost every stage.
Researchers can discover more potential weaknesses.
They can validate candidates faster.
They can generate proof-of-concept code faster.
They can document findings faster.
The funnel is therefore becoming wider at the top.
The challenge is ensuring that the bottom of the funnel does not become overwhelmed.
Triage May Become the
The future may not be limited by vulnerability discovery.
It may be limited by verification.
Companies could eventually receive more potentially useful vulnerability information than humans can reasonably evaluate.
That makes automated triage one of the most strategically important technologies in the bug bounty industry.
Human Judgment Will Remain the Final Firewall
AI can rank a report.
It can summarize evidence.
It can identify suspicious code.
But someone still needs to determine whether a vulnerability actually matters.
That decision has legal, operational, financial, and security consequences.
Human judgment will therefore remain central.
AI vs. Humans Is the Wrong Debate
The real competition is becoming:
Researchers using AI vs. researchers not using AI.
That is a much more uncomfortable conclusion.
AI may not eliminate security researchers.
It may eliminate some traditional security-research workflows.
Researchers who adapt could become dramatically more productive.
Researchers who refuse to adapt could find themselves competing against people who can perform the same tasks much faster.
The Definition of a Skilled Hacker Is Changing
A modern researcher increasingly needs several skill sets.
They need vulnerability knowledge.
They need programming ability.
They need operating-system knowledge.
They need exploitation skills.
They need analytical judgment.
And increasingly, they need the ability to build effective AI-assisted workflows.
That combination will become extremely powerful.
The $30,000 Bug May Become a $5,000 Bug
The macOS examples demonstrate the potential severity of the economic shift.
When a vulnerability category experiences a dramatic price reduction, researchers cannot simply work harder.
They have to reconsider the entire business model.
The number of hours required to produce a finding must be compared with its expected reward.
That calculation will increasingly determine what researchers choose to investigate.
Researchers Will Chase Scarcity
If common vulnerability classes become cheap, researchers will naturally move toward areas where AI has difficulty.
That could mean highly complex exploit chains, hardware security, unusual protocols, novel operating-system behavior, advanced sandbox escapes, and vulnerabilities requiring deep contextual understanding.
Scarcity creates value.
The New Premium Will Be Complexity
Simple vulnerabilities could become commodities.
Complex vulnerabilities could become premium assets.
That is not necessarily bad for cybersecurity.
It could push serious researchers toward deeper technical work.
But it may also make the field harder for newcomers to enter.
Entry-Level Research Could Become More Difficult
Ironically, AI makes starting vulnerability research easier while potentially making sustainable professional research harder.
A beginner can now access sophisticated assistance.
But that same beginner enters a market filled with other AI-assisted researchers.
The barrier to entry falls.
The competition barrier rises.
The Bug Bounty Economy Is Becoming More Professional
The old image of the lone hacker discovering a bug from a laptop is increasingly incomplete.
Modern bug hunting resembles a small research operation.
Researchers need automation.
They need pipelines.
They need data management.
They need prioritization.
They need repeatable methodologies.
They need reputation.
AI is accelerating this professionalization.
Companies Also Need to Adapt
The responsibility cannot fall entirely on researchers.
Companies must improve their vulnerability intake systems.
They need better triage.
They need clear program rules.
They need realistic reward structures.
They need fast communication.
And they need mechanisms that prevent low-quality submissions from overwhelming serious research.
A broken bounty program ultimately hurts the company as much as the researcher.
Bug Bounties Still Have a Strong Future
Despite the disruption, there is little evidence that bug bounties are disappearing.
The incentive remains powerful.
Companies need external researchers because internal security teams cannot realistically test every possible attack path.
Independent researchers bring different perspectives.
AI does not eliminate that need.
It changes the scale.
The Next Generation Will Be AI-Native
The next generation of elite researchers may never remember a world where vulnerability research was performed entirely manually.
For them, AI-assisted code review, reconnaissance, reverse engineering, fuzzing, and exploit analysis will simply be normal tools.
That could produce researchers with extraordinary productivity.
The challenge will be teaching them not to confuse AI assistance with actual understanding.
Deep Analysis
Build a Safe Research Workflow
For authorized security testing, researchers can begin with a structured workflow rather than allowing an AI model to operate without supervision.
Create a dedicated workspace
mkdir -p ~/security-research/{notes,scans,evidence,reports}
Record the target scope
cat > ~/security-research/notes/scope.txt <<'EOF' Target: AUTHORIZED_TARGET Scope: AUTHORIZED_ASSETS_ONLY Testing: NON-DESTRUCTIVE EOF
Verify Before Reporting
Potential findings should be reproduced independently before being submitted.
Preserve evidence hashes sha256sum evidence/ > evidence.sha256
Review HTTP response headers
curl -I https://AUTHORIZED_TARGET/
Save a controlled response for analysis
curl -sS https://AUTHORIZED_TARGET/ \n-o evidence/response.txt
These commands are intentionally limited to assets you are authorized to test. The goal is evidence preservation and validation, not uncontrolled exploitation.
Compare Expected and Actual Behavior
A useful research technique is to compare requests made under different authorized roles.
curl -i \n-H "Authorization: Bearer AUTHORIZED_TOKEN" \nhttps://AUTHORIZED_TARGET/api/resource
The important question is not whether two requests return different data.
The important question is whether the difference violates the application’s intended authorization model.
Search Source Code Systematically
For code you are authorized to audit, simple static searches can help identify areas that deserve deeper manual review.
grep -RniE \n'eval(|exec(|system(|deserialize|pickle|subprocess' \n./src/
This does not prove that a vulnerability exists.
It creates hypotheses.
The researcher must then determine whether attacker-controlled data can actually reach the relevant code and whether a meaningful security boundary is crossed.
Use AI as a Copilot
A strong AI-assisted workflow should look more like this:
Source code
↓
Static analysis
↓
AI-assisted hypothesis generation
↓
Human review
↓
Controlled reproduction
↓
Impact validation
↓
Evidence collection
↓
Human-written final assessment
The critical principle is that AI should accelerate investigation rather than replace verification.
Measure Your Own Signal-to-Noise Ratio
Researchers can also track their submission quality over time.
Valid reports / Total reports × 100 = Validation Rate
If a researcher submits 20 reports and only two are legitimate, the answer is not necessarily to submit 200.
The better strategy is to improve the research process.
A high-quality researcher with a high validation rate can become more valuable than a researcher producing enormous volumes of speculative reports.
✅ AI Has Increased Vulnerability-Research Productivity
The
✅ Submission Volumes Have Increased Dramatically
The reported increases from major bug bounty and vulnerability-research organizations support the claim that the industry has experienced a substantial rise in submissions. However, individual platform statistics represent specific periods and organizations, so they should not automatically be interpreted as a permanent universal growth rate.
✅ AI-Generated Low-Quality Reports Are a Real Problem
The curl example illustrates the problem particularly clearly. More automated report generation can produce more false positives and low-quality submissions, increasing triage costs and potentially slowing legitimate researchers.
❌ The “Vulnpocalypse” Does Not Mean Bug Bounties Are Dying
There is insufficient evidence for such a conclusion. Rising submission volumes and falling prices for certain vulnerability classes can coexist with increasing total payouts and growing numbers of highly successful researchers.
⚠️ Mid-Tier Vulnerabilities Face the Greatest Economic Pressure
This is a strong interpretation rather than a universal law. The available examples suggest that some mid-tier vulnerability categories are experiencing price compression, but bounty prices remain highly dependent on vendor, severity, exploitability, product, program rules, and market demand.
Prediction
(+1) AI-Assisted Research Will Become the Standard
Within the next few years, serious vulnerability researchers will likely treat AI as a normal part of their toolkit, much like debuggers, proxies, fuzzers, static analyzers, and reverse-engineering platforms.
(+1) Elite Researchers Will Become More Productive
The strongest researchers are unlikely to be replaced outright. Instead, AI will remove repetitive work and allow them to spend more time investigating difficult vulnerability classes and sophisticated exploit chains.
(+1) Security Research Will Move Toward Volume and Automation
Researchers who can build reliable automated pipelines will have an advantage. Finding dozens of smaller vulnerabilities may become economically attractive when AI dramatically reduces the time required to investigate each candidate.
(+1) AI-Generated Software Will Create New Attack Surfaces
As AI-generated code becomes more widespread, security researchers will have more software to inspect. Poorly reviewed AI-generated code could create a significant new source of vulnerabilities.
(-1) Mid-Tier Bounty Prices Will Remain Under Pressure
Common vulnerability categories are likely to experience continued price compression as more researchers gain access to powerful AI-assisted discovery tools.
(-1) Low-Quality Submissions Will Become an Even Bigger Problem
Unless platforms improve automated filtering, AI could continue increasing the number of reports that consume triage resources without providing meaningful security value.
(-1) Some Independent Researchers May Leave the Market
Researchers who rely heavily on occasional mid-value findings could struggle if rewards decline while competition increases. The people most likely to survive will be those who diversify their skills, automate their workflows, and target harder problems.
(+1) The Bug Bounty Economy Will Survive the Vulnpocalypse
The most likely outcome is not collapse but transformation.
Bug bounties will remain valuable because software will continue to contain vulnerabilities, attack surfaces will continue to expand, and organizations will continue to need independent security perspectives.
The difference is that tomorrow’s successful researcher may look less like a lone hacker manually searching for one perfect bug and more like the operator of an AI-assisted security research laboratory.
The vulnpocalypse may therefore destroy the old bug bounty economy without destroying bug bounty hunting itself.
The middle class may shrink in some areas, bounty prices may continue to fluctuate, and low-quality submissions may overwhelm traditional workflows. But the underlying demand for genuine security expertise is unlikely to disappear.
The real dividing line will be simple: AI can help find possibilities, but researchers who can prove what is real will remain the ones companies are willing to pay for.
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