BugQore finds defects and vulnerabilities across your codebase, generates fixes, and validates them with tests and reachability analysis — with a full evidence trail for your engineers to review.
BugQore doesn't run a single model that guesses. It orchestrates a team of specialized AI agents that collaborate, challenge each other, and verify every step.
Hunts defects and vulnerabilities by combining static program analysis with retrieval over CVE and advisory data, then confirms each one is reachable in your code.
01 · DetectGenerates precise, minimal patches that address the root cause of an issue rather than silencing its symptoms — with a reproducible trigger for each one.
02 · RepairExercises every patch against your test suite in a sandbox — capturing the results your engineers will review before anything reaches production.
03 · ValidateApplies reachability and policy checks, then packages the change with full reasoning and evidence for your engineers to approve.
04 · EvidenceLink your repositories, CI, and issue tracker. BugQore indexes your code, APIs, and dependencies, and maps them against known vulnerability data.
Agents continuously analyze your code for defects and trace which findings are reachable, enriched by retrieval over CVEs, docs and your internal policies.
Patches are generated, then exercised against your test suite in an isolated sandbox, with a reachability analysis showing whether the defect was actually exploitable.
Your team receives a ready-to-merge pull request with the full evidence chain — what was found, why, and the test results behind the fix.
Five things actually running in production — each one something we can show you in a demo.
Four specialized agents — finder, fixer, tester, reviewer — hand work to each other in sequence, so no single model both writes a fix and signs off on it.
A static pre-pass narrows the search space before any model runs, and a reachability analysis establishes whether a defect is actually callable from real entry points.
Every patch is exercised against your test suite in an isolated sandbox before a human reviews it — and the raw results ship with the pull request.
Findings are grounded in retrieved CVE records, advisories, and your internal policies rather than in what a model remembers about them.
Agents read and edit code, run scanners and compilers, and execute test suites — grounding each decision in real tool output instead of a guess.
Generic AI assistants suggest code. BugQore ships evidence. Every finding comes with a root-cause explanation and a reproducible trigger, every fix with test results, and every action with an audit trail.
The compilers, analyzers, and research that make BugQore possible were built in the open. Contributing back isn't a side project for us — it's how we work. BugQore's AI has contributed security fixes now merged into web2py, Angular, Apache Arrow, Apache Airflow, pip, libjxl, Firebase, and npm — including LDAP-injection, prototype-pollution, path-traversal, and RCE-class fixes.
Every contribution discloses its AI authorship, and every vulnerability we find in the wild goes through responsible disclosure first. Each fix below links to its merged pull request — go read the diffs.
Follow our work on GitHubmarshal.loads reachable via the ticket allowlist
web2py
__proto__
Angular
SQLFORM.grid view/edit/delete to its own tables
web2py
Authorization header on CONNECT proxy requests
async-http-client
Also merged: XSS sanitization in Ember.js, memory-safety fixes in libjxl, path-traversal hardening in Firebase, and purl injection in npm.
BugQore is headquartered in Bengaluru, India — one of the world's largest engineering talent hubs. Our team brings together researchers and engineers across AI, programming languages, and security, united by one conviction: software reliability should be demonstrable, not hoped for.
If we can't verify it, we don't ship it. Claims come with evidence.
We owe our foundations to open source, and we pay that debt forward.
Our AI does the heavy lifting; your engineers keep the final say.
We're onboarding early partners now. Tell us about your stack and we'll show you what AI-driven reliability engineering looks like in practice.