A RunSybil alternative you can steer
RunSybil sits at the fully-autonomous pole of AI pentesting, with minimal human steering. Operator keeps that autonomy's breadth and adds a human on the loop, so you can direct the agent at the business logic and edge cases a pure-autonomous run tends to miss, with proof attached to every finding.
Fully-autonomous versus steerable autonomy
Both run as autonomous AI pentesters, so both give you breadth and constancy. The difference is control. RunSybil represents the pure-autonomous pole; Operator is human-on-the-loop, a named category between fully-autonomous and human-validated.
| Operator | RunSybil | |
|---|---|---|
| Autonomy model | Steerable, human-on-the-loop | Fully-autonomous, minimal steering |
| Business-logic depth | Direct the agent at context-specific abuse | Strongest on broad, known classes |
| Breadth | Autonomous, continuous | Autonomous, continuous |
| Reporting | Exploit-proven: request/response, repro, CVSS v3.1 | Autonomous findings |
| Pricing | Published, self-serve free first scan | Not publicly published |
| Human validation | On demand | Not the primary model |
RunSybil is a well-funded, fully-autonomous AI pentest startup (around $40M in funding, third-party estimate, as of 2026). The contrast here is about steering model, not capability to fund the work.
A serious, well-funded take on autonomous testing
RunSybil is a credible player at the fully-autonomous pole. For teams that want breadth with as little human involvement as possible, a pure-autonomous agent is a coherent design, and RunSybil has the funding to invest in it.
We share RunSybil's core premise: autonomy is the right way to get continuous breadth across a changing attack surface. Where we differ is that we think the best results come from keeping a human able to steer, not from removing the human entirely.
- Autonomous breadth. Continuous coverage across a changing surface with minimal human effort.
- Well-capitalized. Around $40M in funding (third-party estimate) behind the fully-autonomous approach.
- Category conviction. A clear, committed take on the fully-autonomous pole of the market.
- Low operator overhead. A fit for teams that want to point-and-forget rather than direct the run.
Autonomy's breadth, with a hand on the wheel
Fully-autonomous runs are strong on known vulnerability classes. What they tend to miss is context: the multi-step abuse tied to how your product actually works, and the edge cases a human would prioritize. Steering closes that gap without giving up breadth.
- Steerable, human-on-the-loop. Direct the agent at specific business logic, priorities, and edge cases a pure-autonomous run would skip, while keeping autonomous breadth underneath.
- Proof-first reporting. Every finding is exploit-proven, with the request and response, reproduction steps, and CVSS v3.1 severity. Anything it cannot reproduce does not reach your report.
- Continuous by default. Operator re-tests as your surface changes, so exposure from drift and routine deploys is caught the week it ships.
- Self-serve, with a free first-scan on-ramp. Start on the free first scan without a sales call, then move up as needed. Paid tiers scale by endpoint volume, so coverage grows with your attack surface.
- Recognized method. Structured against OWASP WSTG, API Top 10, and ASVS, PTES, NIST SP 800-115, MITRE ATT&CK, and CVSS v3.1.
- Human validation on demand. Route any finding, or a full run, through a senior practitioner when you want a person's signature on the result.
Common questions
How is Operator different from RunSybil?
RunSybil represents the fully-autonomous pole of AI pentesting, with minimal human steering. Operator is steerable and human-on-the-loop: it keeps autonomy's breadth but lets a human direct it at business logic and edge cases a pure-autonomous run tends to miss.
Does steering slow the agent down?
No. Operator runs autonomously for breadth by default. Steering is optional and human-on-the-loop, so you can point it at priorities or edge cases without giving up the constancy of an autonomous run.
What does fully-autonomous miss that steering catches?
Pure-autonomous runs are strong on broad, known vulnerability classes but can miss context-specific business logic, multi-step abuse tied to how your product actually works, and edge cases a human would prioritize. A human-on-the-loop can direct the agent at exactly those.
How does Planck report findings?
Every finding is exploit-proven and proof-first: it ships with the request and response, reproduction steps, and a CVSS v3.1 severity. Anything the agent cannot reproduce does not reach your report.
How do I get started with Planck?
Start on the self-serve free first scan without a sales call. Paid tiers (Pro, Annual Assessment, and Enterprise) scale by endpoint volume, so coverage grows with your attack surface rather than a fixed package.
Other alternatives, compared
Every comparison on this site is judged on one thing first: whether each finding ships a runnable proof-of-concept you can re-run yourself. See how Operator tests for BOLA and BFLA.
Keep the autonomy. Add the steering.
Point Operator at your surface for breadth, then direct it at what matters, with proof on every finding.