Astra is a Penetration Testing as a Service (PTaaS) platform that delivers a compliance certificate. For small teams that want a human pentest so they can be audit-ready, it does the job.
But according to Aikido's 2026 State of AI in Pentesting report, more than half of security leaders say manual pentests often or always miss logic flaws, broken access controls and multi-step vulnerabilities, rising to 92% for teams shipping multiple times per day. Most teams today are looking for AI pentesting, which allows deeper tests to run more frequently. In Aikido's Autonomous vs. Manual Pentesting Benchmark, AI pentests completed in hours, while manual tests took days to weeks.
Astra's core pentesting product is manual, which limits its depth and slows its cadence. Its autonomous pentesting product launched in June 2026, but it is still behind a waitlist, and even once it becomes generally available, it enters a market where competitors have been iterating on AI pentesting in production for months.
We'll look at different Astra alternatives in this post, which include:
- Aikido
- XBOW
- Horizon3
- Cobalt
- Intruder
- RunSybil
- DepthFirst
TL;DR
Aikido is the strongest choice for teams that want enterprise-grade pentesting. Its AI pentesting runs white-box, meaning agents read the source code before attacking, which, across more than 1,000 pentests, uncovered 7x more vulnerabilities than greybox testing alone. In a head-to-head at Tyro Payments, a regulated Australian bank, Aikido's AI pentest finished in five and a half hours and surfaced 30 issues (nine high-severity), while the incumbent human testers took 15 business days to find only five issues, one of which was high. The rest of this list covers XBOW and RunSybil for autonomous web app pentesting, Horizon3 for infrastructure-focused testing, Cobalt for human-led pentesting as a service, Intruder for perimeter vulnerability management, and DepthFirst for an AI-native platform play.
What does Astra Security do
Astra is known for manual penetration testing that produces a verifiable certificate that teams can hand to auditors. Alongside the pentesting service, the platform includes a DAST offering with a vulnerability library Astra puts at 15,000+ checks, plus API and cloud security scanning. For companies that need a compliance-ready pentest report and want basic vulnerability coverage from the same vendor, it covers that ground. Though reviews cite challenges with customer service, slow performance, and a UI in need of a refresh.
In June 2026, the company announced an autonomous pentesting product, a clear sign it sees where the market is moving. Manual pentests that take weeks and test a frozen snapshot of your app don't match how teams actually ship software. But as of writing, the autonomous pentest product page still directs users to a waitlist rather than a live product.
Why teams look for alternatives
AI pentesting is waitlist-only
Astra's autonomous pentesting is currently waitlist-only, and even once it ships, it enters a space where other vendors have been iterating in production for months. Teams need the speed and depth of proven AI pentesting today. 79% of security leaders are concerned about missing vulnerabilities introduced between scheduled tests.
Small team, big claims
Astra has raised roughly $2.8M in total funding, with a $2.7M growth round in early 2025. That's modest for a company promising autonomous pentesting, AI fix agents, and a 15,000+ vulnerability library. None of that means the product can't work, but it's worth asking how much R&D is behind the features on the roadmap, especially when competitors in this list are shipping with tens or hundreds of millions behind their engineering teams.
Manual pentesting is slow
Astra's own docs put an initial pentest at 10 to 20 business days, so two to three weeks before rescans even begin. For teams shipping multiple times a week, that means the application has changed significantly by the time the report lands. 48% of security leaders saying findings are already outdated when they arrive. AI pentesting platforms like Aikido return results in hours.
Retests come with conditions
Manual rescans must be requested within 30 days of findings, at least 50% of critical and high severity vulnerabilities need to be fixed first, and the rescan itself takes another three to nine business days. If you miss the window, you may need to purchase again depending on your plan.
No broader security platform
Astra covers pentesting and DAST. It doesn't include SAST, SCA, secrets detection, IaC scanning, or cloud posture management, so offensive results live in a silo, disconnected from the rest of your security work.
What to look for in an alternative
AI-driven pentesting
Autonomous agents that reason about application behavior, chain exploits, and produce validated findings with working proofs-of-concepts.
White-box testing
Agents that can read source code before attacking consistently find more. Across more than 1,000 AI pentests, white-box testing (with full source code access), uncovered 7x more vulnerabilities than greybox testing alone.
A product you can actually use today
Astra's autonomous pentesting is behind a waitlist. Check whether you can start a pentest this week, or whether you're signing up for a roadmap. Look at public customer reviews and the funding behind the engineering team. A vendor with a few million in total funding and a feature list that reads like a series B is worth pressure-testing.
Compliance-grade output
If pentesting exists partly to satisfy auditors, the report needs to hold up on its own. Look for structured findings with evidence that you don't have to clean up before handing to a compliance team.
Platform breadth beyond offensive findings
A pentest that lives in its own silo creates triage work somewhere else. Alternatives that also cover SAST, SCA, secrets, cloud posture, and runtime let you see offensive findings alongside everything else in one place.
Top Astra alternatives
Aikido Security
Aikido Security's AI Pentesting runs on autonomous agents that reason through application behavior, chaining multi-step attack paths and validating exploitability through real exploitation rather than pattern matching. Because agents run white-box by default, reading source code before attacking, they find vulnerabilities that black-box and greybox approaches miss.
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Additionally, for teams that need pentesting whenever software changes are made, Aikido Infinite triggers a full pentest on every deployment, so validation keeps pace with the fastest- moving teams. It generates AutoFix PRs with code-level patches, and retests after the fix is merged.
For teams that want pentest-grade reasoning without spinning up a live environment, AI Code Analysis uses the same agentic engine to review source code directly, catching IDORs, broken access controls, and business logic flaws across files and services. It pairs well with Aikido Attack as a way to run regular deep analysis on a codebase at a fraction of the cost and setup of a full pentest.
The research behind the agents is public. Aikido's offensive security team regularly publishes findings, including work on benchmarking AI models against known CVEs and the discovery of eight high-severity vulnerabilities in NodeBB.
Aikido also publishes how its agents are secured. The architecture enforces scope at the network layer rather than relying on prompt-level instructions, separates the control plane from the execution environment, and defaults to staging only. Production has to be explicitly opted into and reviewed before anything runs.
Beyond pentesting, Aikido Security is a platform that covers SCA, SAST, secrets detection, IaC scanning, container image analysis, cloud posture management, and runtime protection. Pentest findings land alongside everything else rather than living in a separate report, and reachability analysis filters out CVEs where the vulnerable code isn't actually called. You can get started in minutes and retests are included, not gated behind a 30-day window.
Best for: Teams that want enterprise-grade AI pentesting for compliance and auditing alongside best-in-class AI code analysis, with fast setup and hands-on support that developer teams actually get to talk to. AI pentest reports are accepted for SOC 2, ISO 27001, HIPAA, and GDPR compliance, so teams can use the same results for both remediation and audits.
XBOW
XBOW focuses on autonomous web application pentesting. Agents explore the target, chain vulnerabilities, and produce findings backed by working exploits, so what lands in the report is validated and actionable rather than theoretical.
There are scope constraints worth knowing. XBOW tests a single credential set per engagement, which means IDORs and permission bypasses that depend on comparing behavior across user roles or tenants may not surface in a single run. Retests are limited to one within a 30-day window, so validating a fix after that requires a new engagement. Coverage is web app and API only, so infrastructure, cloud, and code-level analysis sit outside what XBOW does.
Best for: Teams that want autonomous web application pentesting with proof-of-exploit evidence. Not the right pick if you need infrastructure coverage or same-day results.
For a more detailed breakdown, see our head-to-head comparison.
Horizon3
NodeZero is Horizon3's autonomous pentesting platform. NodeZero covers internal networks, external attack surface, and cloud environments (AWS, Azure, Kubernetes), pivoting through infrastructure and chaining together harvested credentials, misconfigurations, and exploitable vulnerabilities the way a real attacker would. You get full visibility into the pentest as it runs, and findings come with proof-of-exploitation evidence.
NodeZero is strongest on the infrastructure side, covering things like Active Directory attacks, credential abuse, lateral movement, network-level misconfigurations. However, web application testing is still in early access, so if your primary risk lies in the application layer and APIs (IDORs, broken access controls, business logic flaws), that's outside NodeZero's core coverage today.
Best for: Security and IT teams that want autonomous internal network and infrastructure pentesting with broad environment coverage. Not the right fit if your primary risk sits in web applications and APIs.
Cobalt
Cobalt announced an autonomous pentesting product on July 23, 2026, with general availability expected in August. Teams evaluating Cobalt for AI pentesting today are evaluating a product that hasn't shipped yet. Cobalt's roots are in human-led pentesting as a service, where it's built a track record. The Cobalt Core community gives you access to approximately 500 vetted security experts, and retesting is unlimited on demand throughout the contract term.
The question is whether that human-led foundation translates into a strong autonomous product. Cobalt's AI engine draws on its historical dataset of 10,000+ critical and high-severity findings, but human pentesters still review the execution plan and manage scope for each autonomous engagement. That's a different architecture from platforms where agents reason independently through a target. The primary business model is still credit-based annual packages, and credits don't roll over. G2 reviewers flag the credit pricing as expensive for smaller organizations and note gaps in testing depth and real-world application coverage.
Best for: Enterprises that want established human-led pentesting with a large tester pool. Not the right fit for teams that need the depth or cadence of AI pentesting today.
Intruder
Intruder is an exposure management platform built for lean teams. It runs 140,000+ checks across web applications, APIs, cloud infrastructure, and network services. It's good at finding exposed services, known CVEs, and perimeter-level misconfigurations.
In July 2026, Intruder launched AI Pentesting for web applications. The product connects to GitHub or GitLab for white-box testing and promises audit-ready reports in hours. But it's worth considering that this product grew out of issue-level investigation agents released the quarter before, which validated individual scanner findings (injection flaws, client-side attacks, information disclosure) rather than reasoning through an application end to end. The full web application pentesting product is weeks old. It may mature into something strong, but marketing claims at this stage are ahead of production evidence.
The platform also stays firmly in the exposure management lane. There's no SAST, no secrets detection, and no SCA, so any code-side findings live in a separate tool. If your risk is split between your perimeter and your codebase, you're managing two systems.
Best for: Lean security and IT teams that want continuous perimeter vulnerability management at an accessible price point. The AI pentesting product is brand new, and the platform doesn't extend to code-level security.
RunSybil
RunSybil is an AI-native offensive security platform built around an autonomous agent called Sybil. It tests applications, APIs, cloud, and infrastructure by reasoning about systems the way an attacker would, validating findings through live exploitation rather than pattern matching. The platform runs continuously on every deployment, re-evaluating what changed and surfacing newly exploitable risks.
Sybil runs black-box design, which means there's an inherent ceiling to the depth and quality of findings Sybil's pentests will surface. This is especially apparent for the vulnerability classes that only make sense once you know how authorization was supposed to work, such as IDORs and broken access controls in multi-role applications.
Best for: Engineering teams that want AI-driven AI pentesting, but teams that want white-box depth from the start should look elsewhere.
DepthFirst
DepthFirst is an AI-native security platform that covers code analysis, supply chain, secrets detection, dependency firewall, and agentic pentesting in a single system. It supports white-box, grey-box, and black-box testing depending on what you want to give it access to. DepthFirst's agentic pentesting validates findings against the running application by replaying real attack paths. When a fix merges, agents automatically retest to confirm the vulnerability is resolved at runtime.
The company's first in-house security model (dfs-mini1) is initially focused on smart contract vulnerabilities, though the company says early internal evaluations suggest it generalizes to broader security tasks. That generalization hasn't been independently validated yet. AI pentesting went generally available in late 2025, so the track record is measured in months. For teams evaluating today, it's worth running a proof of concept rather than relying on the marketing alone.
Best for: Teams that want a platform covering code, supply chain, secrets, and pentesting in one place, built AI-native from the ground up. Still early on the public track record, so run your own evaluation.

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