We find real vulnerabilities in software from Microsoft, Meta, HuggingFace and more. $62k earned in our first 2 months. Our entire pipeline runs on Claude Opus 4.8 — a similar approach to corporate security teams, at a fraction of the cost.
We achieve comparable results to corporate security research teams at a fraction of the cost. Here's the real math.
| Metric | Anthropic / Meta | OwlMind | Difference |
|---|---|---|---|
| Security research team | 50–200 engineers | 1 founder + 5 researchers | ~30x smaller |
| Annual salary budget | $15M–$60M/yr | ~$30k/yr (projected) | 500x cheaper |
| LLM API spend | Internal (free) | ~$500/mo | They build the models |
| Infrastructure | $1M+/yr (clusters) | $50/mo (1 VPS) | 1,600x cheaper |
| AI model powering research | Internal models | Claude Opus 4.8 | Same tier |
| SAST detectors | Proprietary | 500+ custom YAML | Comparable |
| Verification approach | LLM + execution | LLM + 11 oracles | Same methodology |
| Vuln classes covered | All | 22 classes | Focused scope |
| Total monthly cost | $1M–$5M | ~$1,000 | 1,000–5,000x |
Anthropic/Meta salary estimates based on public Levels.fyi data for security engineers ($200k–$400k/yr). OwlMind costs are current operational expenses. $62k earned in first 2 months of operations.
Our entire pipeline — scanning, verification, report generation — runs on Anthropic's most capable model. Same AI, different budget.
Real revenue from bug bounty research. Working technology, real submissions to 8 platforms, zero marketing spend. Value is in the results.
Solo-developed at Peking University. 130+ scripts, 500+ detectors, full team infrastructure. Proof that AI levels the playing field.
Real security bugs discovered and reported to these companies. All submissions verified with working PoCs.
prepare() bypasses, NO_BACKSLASH_ESCAPES exploits in WordPress plugins.
Missing ownership checks in multi-vendor platforms, LMS systems, booking plugins.
Server-side request forgery in AI frameworks: LlamaIndex, smolagents, Langflow.
Unprotected REST endpoints, AJAX handlers without permission_callback.
Remote code execution via deserialization in Transformers, AutoGen, MCP servers.
XSS, Path Traversal, Object Injection, CSRF, SSTI, Prompt Injection, CQL Injection.
An approach inspired by how corporate security teams use LLMs, but built on a $1k/month budget. Proven on real submissions.
Multi-stage LLM-powered vulnerability verification with 11 execution oracles. Not SAST noise — every finding is proven with a working PoC in a real environment.
| Feature | Corporate Teams | OwlMind Mythos |
|---|---|---|
| LLM-powered verification | ✓ | ✓ |
| Execution-based proof (PoC) | ✓ | ✓ |
| Multi-vote adversarial review | ✓ | ✓ |
| Taint analysis (AST + Psalm) | ✓ | ✓ |
| Docker lab integration | ✓ | ✓ |
| 5-source CVE dedup | — | ✓ |
| Self-learning detector loop | — | ✓ |
| Cost per verification | ~$2.00 | ~$0.15 |
| Monthly platform cost | $50,000+ | ~$1,000 |
Pricing comparison based on publicly available data and internal benchmarks.
From target to bounty payout — mostly automated, always human-verified.
AI scans repos. 500+ detectors, AST analysis, taint tracking find potential vulnerabilities.
Mythos engine: 11 execution oracles + Docker lab PoCs confirm exploitability.
5-source check: WPScan, NVD, ExploitDB, Huntr, OWVD (27k+ CVEs).
Auto-routed to the right bounty platform with CVSS, impact, and working PoC.
Beyond offense. Blue Team capabilities we're building to help organizations defend against the vulnerabilities we find.
Every confirmed vulnerability generates YARA/Sigma detection rules. Ship to your SIEM within hours of discovery.
Step-by-step remediation guides for each vulnerability class. From SQL injection to RCE — actionable response.
500+ YAML detector rules scan your codebase continuously. Self-learning loop improves accuracy with every confirmed finding.
After vendor releases a fix, we verify the patch actually works. No false sense of security from incomplete patches.
Simulated attack scenarios using real vulnerability patterns. Train your SOC team against actual exploit techniques.
Private early-warning advisories for enterprise clients before public disclosure. Time to patch before attackers find it.
Bachelor's student at Peking University (PKU), majoring in AI Engineering. Built OwlMind from zero while still in university. Solo-developed the entire AI pipeline: 130+ scripts, 500+ SAST detectors, Mythos verification engine, DeepHunt orchestrator, and the team infrastructure — all powered by Claude Opus 4.8.
Proof that one student with the right AI tools can build what takes corporations millions of dollars and hundreds of engineers.
Fixed monthly salary + performance-based bonuses. Real money for real work.
KPI bonus is calculated monthly based on: number of confirmed findings, severity level (Critical/High pay more), platform acceptance rate, and report quality score. Top performers can earn 2–5x their base salary in bonuses.
Becoming the global standard in AI-powered security research. Our roadmap to strategic partnerships.
Fixed salary + KPI bonuses. AI-powered tools. Real vulnerabilities in real companies.