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Comparison · RunSybil Alternative

# 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.

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[See plans](https://planckproof.ai/pricing)

How They Differ

## 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.

Where RunSybil Is Strong

## 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.

Why Teams Pick Operator

## 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.

FAQ

## 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.

See Also

## Other alternatives, compared

[XBOW alternative](https://planckproof.ai/xbow-alternative)

[Cobalt alternative](https://planckproof.ai/cobalt-alternative)

[NodeZero alternative](https://planckproof.ai/nodezero-alternative)

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](https://planckproof.ai/bola-testing) and [BFLA](https://planckproof.ai/bfla-testing).

Get Started

## Keep the autonomy. Add the steering.

Point Operator at your surface for breadth, then direct it at what matters, with proof on every finding.

[Get a Quote](https://planckproof.ai/quote)

[Compare all alternatives](https://planckproof.ai/compare)