Remote Code Execution (RCE) via Prompt Injection
PoC: Sent adversarial email text to /api/v1/agent. Agent calls execute_query tool with direct string interpolation of user inputs. Submitting database admin credential drop payload.
Bug bounty programs are experiencing a genuine structural shift — a large majority of researchers now use AI tools to accelerate their work, which is producing both a real increase in genuine, high-quality findings from skilled researchers and a much larger flood of lower-quality, AI-generated submissions that look superficially like a real report but are missing the context, verification, or exploitability that would make them worth an engineering team's time.
The second problem is triage burden. A submission that reads like a polished report but turns out to be unvalidated tool output wastes meaningful engineering and security team time to properly evaluate and dismiss — and when this happens at volume, exhausted triagers either burn out or start dismissing submissions too quickly, risking genuine findings getting lost in the noise.
The third problem is remediation follow-through. A validated, genuine bug bounty finding is only valuable if it actually gets fixed and the fix gets verified — a program that pays out rewards but doesn't have a disciplined remediation and retesting process is accumulating known, unpatched vulnerabilities even while technically "running" a bug bounty program.
We help design and operate bug bounty program processes specifically built for the current environment — clear scope and quality bar guidance that discourages low-effort, AI-generated submission dumping while still welcoming the genuine researchers using AI as a legitimate force multiplier, and a triage process calibrated to separate verified, exploitable findings from unvalidated tool output efficiently.
For validated findings, we build a disciplined remediation pipeline — clear ownership assignment, prioritization by genuine exploitability rather than raw severity score, and mandatory retesting to confirm a fix actually closed the vulnerability rather than just changed its surface presentation. And we help establish the program incentive structure itself — rewarding genuine, well-verified findings clearly enough that skilled researchers keep engaging, while making low-effort, unverified submissions genuinely unattractive to file.
System Features
01.AI-Era Triage Design
Program scope, submission guidelines, and triage processes specifically calibrated to separate genuine findings from AI-generated noise.
02.Finding Validation & Verification
Every submission independently validated and demonstrated before being accepted, ensuring engineering time is spent only on confirmed, exploitable issues.
03.Prioritized Remediation Pipeline
Validated findings assigned clear ownership and prioritized by genuine exploitability and business impact, not raw severity score alone.
04.Mandatory Fix Retesting
Every remediation independently retested to confirm the vulnerability is actually closed, not just superficially addressed.
05.Program Incentive & Scope Design
Reward structure and program scope designed to keep skilled researchers engaged while discouraging low-effort, high-volume submission patterns.
Signal-First Triage Pipeline
Every incoming bug bounty submission passes through a structured verification pipeline before it's treated as a genuine finding — reproducing the claimed vulnerability independently, confirming actual exploitability rather than a theoretical possibility, and checking against known duplicate patterns before it consumes further engineering attention. Submissions that fail this verification are declined efficiently and clearly, rather than lingering in an ambiguous queue that eventually gets attention through pure triager burnout.
This pipeline is specifically designed around the current reality that a meaningful share of incoming reports are AI-assisted, ranging from genuinely excellent (a skilled researcher using AI to move faster and verify more thoroughly before submitting) to genuinely low-effort (unverified tool output submitted with minimal human review). The verification step is what separates the two categories reliably, protecting your team's remediation capacity for the findings that represent real, confirmed risk, while giving prompt, clear feedback on submissions that don't clear the bar — which is also what keeps skilled researchers engaged with your program rather than discouraged by a queue dominated by noise.
// Real-World Use Cases
- >Company running an existing bug bounty program experiencing triage overload from AI-assisted submission volume
- >Organization launching a new bug bounty program needing scope and incentive design built for the current researcher landscape
- >Business with a backlog of unresolved, validated findings needing a disciplined remediation and retesting process
- >Company needing clear metrics on program health — validated finding rate, remediation time, researcher engagement
- >Organization needing bug bounty program support specifically for AI agent or LLM-based products with novel finding categories
// Measurable Business Impact
- ✔Protects engineering and security team time from being consumed by unverified, low-quality submissions
- ✔Ensures validated findings actually get remediated and verified as fixed, not just acknowledged and paid out
- ✔Improves the researcher experience for skilled hunters, keeping genuine talent engaged with your program
- ✔Provides clear, prioritized visibility into real, exploitable risk rather than an undifferentiated report backlog
- ✔Reduces the risk of a genuine critical finding being lost or delayed in a noisy, overwhelmed triage queue
Frequently Asked Questions
Find the signal in the noise
Verified findings, disciplined remediation, researchers who stay engaged.
Scope your bug bounty support