Key Takeaways
- The term AI red teaming now covers two different jobs, testing AI systems for prompt injection and jailbreaks, and pointing AI agents at conventional applications and APIs. This article is about the second.
- Novee is the strongest option because its offensive AI model proves exploitability with a working exploit across web, mobile, API and external attack surface, then retests after the fix.
- Most platforms can only test the APIs they have been handed a document for. OpenAPI, Swagger or GraphQL specs are a hard requirement on several of them.
- Published accuracy figures across this category are vendor-reported with no stated methodology, so the evidence a platform returns per finding is a better buying signal than its headline percentage.
A security team asks for AI red teaming and gets two completely different demos. One vendor shows prompts slipping past a guardrail on a chatbot. Another points an agent at a staging environment and comes back with a chained exploit against an authorization flaw. Both are using the phrase correctly, which is exactly the problem.
The second kind is what this article covers: platforms that use AI agents to attack applications and APIs the way an attacker would, then prove the finding rather than flag it. That category has grown quickly, and the differences between the platforms in it are sharper than the marketing suggests, particularly around how much of your API surface they can reach before you hand them a specification.
Table of contents
At a Glance
- Novee: proprietary offensive AI model proving exploitability across apps, APIs and attack surface
- Invicti: proof-based scanning with an agentic pentest layer on top of enterprise DAST
- PortSwigger: the industry standard scanner, with agentic testing in public beta
- FireCompass: working proof of exploit with a timestamped audit trail and broad surface coverage
- RunSybil: autonomous web and API testing with an MCP server feeding coding agents
- Detectify: attack surface monitoring plus spec-driven application and API scanning
- APIsec: an API-only specialist built around OpenAPI-generated test playbooks
- Terra Security: agentic testing with a human pentester signing off on every finding
What AI Red Teaming Means When the Target Is an Application
The split in this category is by target rather than by technique. Both sides run LLM-driven agents that generate and execute attacks. They aim them at different things and report against different frameworks.
Testing AI systems means generating adversarial inputs against models, system prompts, guardrails, agents and MCP servers, covering prompt injection, jailbreaks, system prompt leakage, tool misuse and data extraction. Findings map to the OWASP Top 10 for LLM Applications and MITRE ATLAS, and the output is a residual risk measurement with hardening recommendations.
Testing applications and APIs means classical offensive security with a new engine behind it. The vocabulary is pentest vocabulary: SQL injection, remote code execution, server-side request forgery, path traversal, broken object level authorization and business logic flaws. Findings map to the OWASP Top 10 and the OWASP API Security Top 10, and the output is an exploited endpoint with a reproducible request and response. The surrounding analyst terms are adversarial exposure validation, continuous automated red teaming and continuous threat exposure management.
The lines do blur in places worth knowing about. Several application-focused platforms have added LLM checks to their scanners, and an MCP server is itself an API, so the two attack surfaces genuinely overlap. The useful disambiguating question is what the tool hands back. A prompt that bypassed a guardrail is one category. An exploited endpoint with a severity mapping is the other.
8 Best AI Red Teaming Platforms for Application and API Testing
1. Novee
Novee is the strongest AI red teaming platform for application and API testing, because it was built around a proprietary offensive AI model rather than an agent layer wrapped over an existing scanner. It tests continuously across web applications, mobile, APIs, AI applications and external attack surface, and it reports findings only when they are proven exploitable, delivering a working exploit rather than a severity score and a confidence rating. Business logic flaws are a stated focus, which matters because that class is invisible to signature-driven scanning and is where authorization mistakes tend to live.
The loop continues past the finding. Agentic remediation produces guidance and auto-retest confirms the issue is actually gone, which the company describes as an end-to-end loop that removes critical risk rather than cataloguing it. Novee reports that its model outperformed general-purpose frontier models by more than 55 percent on constrained web exploitation challenges, with accuracy up to 90 percent against roughly 65 percent for general models, and that it has uncovered hundreds of novel vulnerabilities for customers.
Key features:
- Proprietary offensive AI model covering web, mobile, APIs, AI applications and external attack surface
- Findings reported only when proven exploitable, delivered with a working exploit
- Business logic and authorization testing rather than signature matching
- Agentic remediation with automatic retest to confirm the fix closed the exposure
- Continuous testing rather than a point-in-time engagement
2. Invicti
Invicti’s Application Security Platform spans scanning, runtime testing and vulnerability management, and the piece relevant here is Proof-Based Scanning, which confirms a vulnerability by safely exploiting it and records the result in a Proofs node, returning artifacts such as a database name for SQL injection or file contents for local file inclusion. Invicti Agentic Pentest, announced in July 2026, adds a coordinator that plans an attack and runs parallel agents per vulnerability class, suppressing findings a standard scan would already have caught.
Key features:
- Proof-Based Scanning confirming vulnerabilities by safe exploitation, recorded in a Proofs node
- Agentic Pentest running parallel agents per vulnerability class with a combined report in 24 hours
- API testing across OpenAPI, Swagger, RAML, Postman, WSDL, GraphQL and protobuf specs
- Granular retest from a single finding, a scan, or in bulk, with Fixed and Rediscovered statuses
3. PortSwigger
Burp Suite DAST, renamed from Burp Suite Enterprise Edition in April 2025, runs the Burp Scanner engine on a schedule or inside a pipeline, with a Chromium crawler that handles single-page applications, dynamic forms and GraphQL. API inputs accept Postman collections, OpenAPI definitions, SOAP WSDL and GraphQL schemas, with integrations for Amazon API Gateway, Azure API Management and Apigee.
Key features:
- Burp Scanner engine with a Chromium crawler for single-page applications and GraphQL
- API scanning from Postman, OpenAPI, WSDL and GraphQL inputs
- Request and response evidence per finding, reproducible in Burp Suite Professional
- Issue statuses driving automatic retest across scans, including Regressed and Fixed
4. FireCompass
FireCompass runs an agentic platform spanning web application and API pentesting, mobile, infrastructure and external attack surface discovery, with modules for continuous automated red teaming, adversarial exposure validation and pentesting as a service. Its discovery pulls API endpoints from JavaScript files and live traffic rather than from an uploaded specification.
Key features:
- API endpoint discovery from JavaScript files and traffic rather than an uploaded spec
- Working proof of exploit with reproduction steps and ready-to-run Python per finding
- Timestamped audit trail of commands, requests and responses for every test
- Scope enforcement, non-destructive validation, rate limits and blast-radius controls
5. RunSybil
RunSybil’s platform, Sybil, runs a hierarchy of agents that each own a domain such as authentication, business logic or injection, then chain findings into exploit paths across the web and API attack surface. It was founded by OpenAI’s first security research hire and a former Meta offensive security lead, and raised 40 million dollars in March 2026.
Key features:
- Agent hierarchy per vulnerability domain, chaining findings into full exploit paths
- Request and response pairs, exploit chains, validation logs, CVSS 3.1 and CWE per finding
- MCP server driving a Fix with AI flow into Codex, Claude Code and Cursor
- Change-scoped continuous testing triggered by a pull request, a manual run or a cadence
6. Detectify
Detectify combines external attack surface management with application and API scanning, drawing its payloads from more than 1,765 modules contributed by a network of several hundred vetted ethical hackers. Surface Monitoring runs continuously across subdomains, IPs, ports and certificates without requiring a pre-existing inventory, and Internal Scanning brings the same engine inside the network through a deployed agent.
Key features:
- Surface Monitoring running continuously with no pre-existing asset inventory required
- Payload modules contributed by a vetted ethical hacker network
- MCP server generating a patch and triggering a validation scan to confirm the fix
- Published annual pricing tiers with per-target add-ons for application and API scanning
7. APIsec
APIsec is the only API-only platform in this list. Its bot parses an OpenAPI specification, generates custom validation playbooks for each API, then runs them against the live service, targeting the OWASP API Security Top 10 along with business logic flaws, role assignment, multi-tenancy and access control. Broken object level authorization and role-based access control are named explicitly.
Key features:
- Playbooks generated per API from an OpenAPI specification and run against the live service
- OWASP API Security Top 10 coverage including BOLA, RBAC and multi-tenancy checks
- Retest workflow where a new scan, not a manual close, transitions the ticket
- Integrations with Jira, GitHub Issues, Linear and Wrike
8. Terra Security
Terra’s platform coordinates a swarm of AI agents across web applications, internal and external networks and AI systems, with human pentesters at decision points in a model it calls human-on-the-loop. Every finding is validated and signed off by a pentester before it reaches the customer, and each test action and human decision is logged to produce a defensible execution record.
Key features:
- Agent swarm with human pentester validation and sign-off on every reported finding
- Reproduction steps and the exact payload or bypass included per finding
- Logged test actions and human decisions producing a defensible execution record
- Closed-loop remediation re-testing automatically after a fix is deployed
How These Platforms Differ on API Coverage and Evidence
Two questions separate this list more reliably than accuracy claims do, because every headline percentage in this category is vendor-reported with no published methodology.
The first is whether a platform can test an API it was never handed documentation for. Undocumented and forgotten endpoints are where real exposure accumulates, and a spec-driven scanner tests precisely the surface you already knew about. The second is what arrives with a finding, since a working exploit, a request and response pair and a severity rating represent three very different standards of proof for the engineer who has to act on it.
| Platform | Undocumented API endpoints | Evidence returned | Testing model |
|---|---|---|---|
| Novee | Covered via attack surface testing | Working exploit | Continuous, autonomous |
| Invicti | Spec required | Proof artifact, injection classes | Continuous plus per assessment |
| PortSwigger | Spec or crawler discovery | Request and response | Scheduled and pipeline triggered |
| FireCompass | From JavaScript and traffic | Working exploit plus Python | Continuous, optional human |
| RunSybil | Not documented | Exploit chain and validation log | On demand, then change scoped |
| Detectify | Discovered but not tested | Reproduction steps | Continuous and scheduled |
| APIsec | Spec at a public URL required | Not documented | Continuous |
| Terra Security | Not documented | Payload and reproduction steps | Continuous, human sign-off |
Read Next
A few related pieces worth your time:
- The Role of Identity Management in Modern Technology
- Traditional Antivirus to EDR: How to Start Protecting Your Endpoints
- 11 High-Rated Cloud Security Services Built for Zero Trust Networks
Frequently Asked Questions
What is AI red teaming?
It describes two different practices. One tests AI systems themselves for prompt injection, jailbreaks and tool misuse. The other uses AI agents to attack conventional applications and APIs the way an attacker would, reporting exploited endpoints rather than bypassed guardrails. Checking which one a vendor means is the first qualifying question.
How is AI red teaming different from DAST?
A DAST scanner sends known payload patterns at an application and reports what looks vulnerable, which generates findings a human then has to confirm. An agentic platform plans an attack, chains steps together and attempts exploitation, so it can reach authorization and business logic flaws that signature-driven scanning misses entirely.
Can these platforms test APIs without an OpenAPI specification?
Several cannot. Detectify, Invicti and APIsec all require an uploaded or hosted spec for API testing, and APIsec needs it at a publicly reachable URL. Others discover endpoints from JavaScript files, live traffic or attack surface mapping. This matters because undocumented endpoints are rarely the ones already covered.
What counts as proof that a vulnerability is real?
The strongest standard is a working exploit with the request and response that produced it, plus reproduction steps. Weaker standards include a confidence rating, a payload that triggered an unusual response, or an AI filter that removed suspected false positives before reporting. Ask which one a platform actually delivers.
Should AI red teaming replace manual penetration testing?
It changes what manual testing is for rather than removing the need for it. Continuous agentic testing covers regression and breadth between engagements, while human testers remain valuable for novel business logic, physical and social engineering scope, and signed attestations that some auditors still require.
