Best AI Pentesting Tools in 2026

Software development ships faster than ever with AI-assisted coding. Many companies now push updates daily or weekly, but security testing hasn't kept pace.

We hear from security teams quite often that they now face an impossible backlog: "I have 1500 applications to pentest with a team of 12..." or "I have only 15 minutes to manually test each new feature release..."

That's why teams are turning to AI pentesting tools. These platforms use AI agents and machine learning to automate attack workflows, scale across APIs and modern web apps, and continuously re-test as code ships.

Modern AI-powered pentesting tools augment the work of pentesters, enabling testing at scale. According to Jyoti Raval, Director of Cyber Security Engineering at Baker Hughes, we interviewed recently: "AI is already transforming pentesting. If you look at automated reconnaissance and scanning, finding those low-hanging fruits, or threat intelligence correlation, they do really well. They can even do fuzzing, exploit generation, and documentation—these are a few clicks away now."

This article reviews the 7 best AI pentesting tools in 2025, examining their capabilities, strengths, and ideal use cases for security teams.

TL;DR - Best AI Pentesting Tools (2026) at a Glance

To help you choose the right AI pentesting solution, this comparison breaks down each tool's core capabilities, trade-offs, and ideal scenarios:

What are AI Pentesting Tools?

AI pentesting tools are security testing platforms that use artificial intelligence and machine learning to automate penetration testing. Unlike traditional scanners, these tools can learn application behavior, adapt their attack strategies, and identify vulnerabilities that typically require human intuition.

Think of them as 24/7 pentesters. They handle the repetitive, time-consuming parts of pentesting—crawling applications, testing thousands of input combinations, tracking authentication flows, and documenting findings—while security teams focus on complex threats and remediation.

Modern AI-powered pentesting solutions can:

• Understand application context and business logic, not just surface-level vulnerabilities
• Adapt attack patterns based on what they discover during testing
• Test continuously as code changes, catching issues before production
• Scale across hundreds or thousands of applications simultaneously
• Generate exploit chains by connecting multiple vulnerabilities

The key difference from older automated tools? AI pentesting platforms reason about how applications work, predict where weaknesses might exist, and adjust their approach in real-time—similar to how an experienced pentester thinks through an engagement.

That said, AI pentesting tools aren't replacing human pentesters. They're augmenting them, handling the grunt work so security professionals can focus on sophisticated attacks, business risk analysis, and strategic security improvements.

What to look for in an AI pentesting tool?

Not all AI pentesting tools are created equal. Here's what actually matters:

• Detection of business logic vulnerabilities: The baseline is whether the platform can consistently detect BOLA, IDOR, privilege escalation, and workflow bypasses the flaws, so you actually can reduce the time of manual pentesting without losing value.
• Scales with your needs: Handles 10 applications or 1,000 without requiring a dedicated team to manage it.
• Signal over noise: AI should reduce alert fatigue, not create it. Look for tools that prioritize findings by actual risk, provide context for remediation, and eliminate false positives through intelligent validation, not just "dump" thousands of potential issues.
• Integration and workflows: The tool should fit your existing stack. Does it work with your issue tracker, send findings to Jira or Slack, and integrate with other security tools, like Wiz? Friction kills adoption.
• Validation and Proof: Demand evidence. Ask for proof-of-concept exploits, not just vulnerability reports. The best AI security testing tools show you exactly how an attacker could exploit the issue, making it easier to understand severity and prioritize fixes.
• Authentication resilience: A lot of modern applications sit behind MFA, SSO, and rotating tokens. A platform should persist across these automatically, not collapse when a new tab is opened or when another user logs in.
• Continuous Testing: You need a solution that integrates with your CI/CD pipeline, automatically retests when code changes, and suggests to developers remediation code snippets tailored to their development framework. Ask: can it test daily releases without manual intervention?

The right AI pentesting solution makes your security team a "team on steroids".

Top 7 AI Pentesting Tools

Here’s a look at the best AI pentesting tools in 2025:

Escape

Escape is an AI pentesting tool, specializing in the detection of business logic flaws and handling complex authentication scenarios. Its approach extends from code to cloud, covering APIs, SPAs, and distributed application environments.

The platform’s AI-driven engine models real application behavior across roles, sessions, and states, enabling the discovery of issues like BOLA, IDOR, and access control. Findings are delivered with in-depth exploit paths, framework-tailored code snippets, and link to its owners and assets, supporting faster developer remediation of critical applications.

Escape is particularly suited for security and AppSec teams aiming to scale vulnerability detection, while maintaining high accuracy even for complex business logic findings and actionable results.

Strengths

✅ Purpose-built for detecting business logic vulnerabilities: Escape’s proprietary engine identifies deep logic flaws such as IDORs, SSRFs, and broken access controls that require real interaction to uncover.

✅ True positives with evidence: Each issue includes AI-powered proof of exploit and remediation guidelines, so engineers can follow the exploit path directly and validate it with confidence.

✅ Attack Surface Management integration: Vulnerabilities are linked to assets discovered across code repositories and cloud integrations, tied to their owners, and weighted by business criticality.

✅ CI/CD-ready reproduced complex exploits: Teams can reproduce complex exploits from bug bounty reports that evolve with their applications and run them automatically in CI/CD pipelines without manual upkeep.

✅ Developer-ready remediation: Escape generates stack-specific code fixes. Whether for Node.js APIs or GraphQL services, developers receive context and patches tailored to their framework, reducing backlog and friction between security and engineering.

✅ GraphQL-native security testing: The platform is also purpose-built for GraphQL security, sending context-aware queries that reflect real application logic.

Org Fit

Mid-to-large enterprises: built for lean security teams deploying updates weekly or daily, and especially well-suited for organizations with complex environments—such as domains and subdomains scattered across multiple teams, applications hosted in various locations and repositories (including monorepos)—where blind spots are hard to detect without context.

Testing Approach

Escape’s engine is rooted in its proprietary Business Logic Security Testing algorithm and uses reinforcement learning with generative AI to adapt requests in real time. The result: a stateful, event-driven exploration engine that doesn’t just ping endpoints, it simulates users interacting with the application, surfacing flaws where real attackers would.

Limitations

❌ Advanced features may require security expertise or training for optimal use.

🟡 Scope is focused on testing APIs, Web Apps, Hosts, and Ports 

❌ Integration coverage for some operational tools is still being expanded

Reviews

“We’ve reduced time spent on pentests from 4–5 days to under half a day.” - Head of Offensive Security, large logistics company

"We saw Escape being a lot smarter, understanding what’s happening, where it is located. For example, it’s finding a billing API, it’s found what it thinks is a billing ID, like 001, and it tries a few other IDs to see if it has access to get some other people’s billing info. It’s a lot more understanding of what’s happening where it’s at. I think this is where tooling and security tooling overall is going.” - Nick Semyonov, PandaDoc

XBOW

XBOW is an AI-powered penetration testing platform that frames itself as a “human-level security tester at machine speed.” Instead of relying on a single scanner, XBOW coordinates hundreds of autonomous AI agents, each focused on a specific attack vector. These agents collaborate to discover vulnerabilities, attempt exploit paths, and validate them with proof-of-concept payloads. The platform emphasizes adversarial realism, aiming to replicate how hackers would approach an application but at a scale and speed no manual team could match. Its positioning is strongest in red-team style scenarios: testing breadth, chaining potential, and rapid validation of impactful exploits.

Strengths

✅ Adversarial realism: Specialized agents run in parallel, chaining attacks, iterating on exploitation paths, and trying to validate them.

✅ Quick launch: Updates can be tested within hours, bypassing the scheduling delays of manual engagements.

✅ Validated exploits: Proof-of-concept evidence is included for vulnerabilities, supporting credibility in findings.

Limitations

❌ Business logic blind spot: Less systematic detection of Business Logic Vulnerabilities (BOLA, IDOR, access control) compared to purpose-built engines.

❌ Context missing: Findings are raw; results aren’t tied into ASM context like asset ownership or prioritization.

❌ Pricing: Priced per action, which makes costs rise quite high for organizations that need to test often.

❌ Developer handoff gap: While exploits are validated, reports don’t provide developer-ready fixes

❌ Engineering adoption: Offensive-first framing resonates with pentesters, but less so with product and developer teams seeking integration into workflows.

Testing Approach

Multi-agent, adversarial exploration with coordinated exploit chaining. Strong for simulating attacker breadth; weaker for systematic, evidence-driven detection.

Org Size Fit

• Organizations with dedicated security or red teams that want adversarial testing without testing too often
• Less optimized for engineering-led orgs where remediation and developer workflow integration are critical.

Terra Security

Terra Security positions itself as an "agentic AI pentesting" platform that blends AI-driven automation with human oversight. Its model deploys a swarm of AI agents that adapt to business logic and system behavior, but keeps a human in the loop to validate and guide outcomes. Unlike legacy scanners, Terra emphasizes context: vulnerabilities are scored not just by technical severity, but by business impact, probability, and exploitability. Its output is tailored for enterprise needs, with compliance-ready reporting for SOC 2 and ISO The platform appeals most to organizations seeking a balance of automation and auditor-friendly assurance.

Strengths

✅ Coverage:  Agents dynamically adjust attacks based on business logic, system behavior, and app-specific risks

✅ In-depth prioritization capabilities: Prioritizes vulnerabilities based on impact to the organization, including comparable breaches and exploitability.

Limitations

❌ Manual reliance: Human oversight slows testing and prevents full autonomy.

❌ Developer handoff gap: Reports are compliance-oriented, not developer-ready for remediation.

❌ Compliance limitations: quite a new solution on the market, coverage only for SOC2 and ISO at the moment; lack of support for more specialized frameworks like PCI-DSS or HIPAA

❌ Workflow integration: Limited evidence of seamless CI/CD fit compared to engineering-first tools.

❌ ASM context missing: Findings aren’t tied to asset ownership or attack surface, reducing operational prioritization.

Testing Approach

Agentic swarm exploration guided by business logic, supplemented each time by human validation (requires human-in-the-loop). Strong for compliance-driven assessments; weaker for continuous, fully automated workflows at developer speed.

Org Size Fit

• Best for large, regulated enterprises (finance, healthcare, SaaS at SOC 2/ISO scale) that prioritize compliance and business-context risk scoring. 
• Less suited for lean engineering teams that need continuous automation, asset context, and developer-ready fixes.

Reviews

“We’ve been really impressed with Terra Security. Their AI-based penetration testing actually feels like a real security researcher is reviewing our app continuously. ”

Invicti

Overview

Invicti is a long-established AI-powered DAST platform that can be used for "AI pentesting" activities. Its strength lies in scale: Invicti can crawl large portfolios of web applications and APIs, identify common vulnerabilities, and validate many of them automatically using its "Proof-Based Scanning" technology. This reduces the burden of false positives and provides reproducible exploit traces, which makes it more credible than legacy scanners. That said, Invicti remains fundamentally a vulnerability scanner, strong on coverage of surface-level issues, but not designed for deeper business logic flaws or modern workflow chaining.

Strengths

✅ Proof-Based Scanning: Invicti’s signature capability is its "Proof-Based Scanning," which attempts safe exploitation to confirm vulnerabilities and reduce false positives.

✅ Broad Coverage: Supports REST, SOAP, and GraphQL APIs along with traditional web applications.

✅ Integrated platform: Part of a broader security suite that also includes Interactive Application Security Testing (IAST), SAST and SCA

✅ Customizable scans: Offers security check templates and flexible automation options, making it adaptable for enterprise workflows.

Limitations

❌ GraphQL limits: Support for GraphQL is restricted to basic vulnerability types, leaving more complex logic flaws uncovered.

❌ Dependency on existing documentation: Does not support automatic API specification generation, requiring manual uploads.

❌ Remediation gap: Findings lack developer-ready fixes or code snippets, requiring additional manual effort to translate results into action.

❌ Coverage focus: Primarily focused on web applications, with less coverage for modern cloud-native environments

❌ Cost barrier: Higher entry-level cost compared to other tools on the market.

Testing Approach

• Classic DAST scanning enhanced with automated exploitation for validation. Strong for confirming common vulnerabilities at scale; weaker for business logic security testing and API coverage

Org Size Fit

• Suited for mid-sized to enterprise organizations that need to continuously scan large portfolios of web apps and APIs. 
• Less aligned for teams prioritizing deep business logic testing, developer-ready remediation, or ASM-driven risk context.

Reviews

"The tool is user friendly and easy to set up. It is very accurate when it comes to discovering vulnerabilities. The support team is very professional and replies quickly. Overall, I'm very pleased with this tool."

Hadrian

Hadrian positions itself as an attack surface-driven automated penetration testing platform. Instead of relying on scheduled scans, its Orchestrator AI triggers tests in real time whenever the attack surface changes - a new asset, configuration drift, or emerging exploit. The platform is designed to mimic adversary behavior, continuously probing assets and validating real exploitation paths. Its emphasis is breadth and responsiveness: showing organizations "what attackers see" and proving impact with contextualized validation.

Strengths

✅ Event-driven testing: Security assessments run automatically when assets or configurations change, reducing blind spots.

✅ Full-attack surface coverage: Goes beyond crown-jewel apps, scanning every exposed asset to prevent lateral movement.

✅ Proof-of-exploit validation: Findings include exploitation paths and evidence, cutting false positives.

✅ Prioritization by impact: Contextual scoring highlights which vulnerabilities matter most, helping teams focus remediation.

Limitations

❌ No business logic support: Doesn’t focus on detecting deep business logic flaws (BOLA, IDOR, access control).

❌ ASM-first orientation: Resonates more with security leaders managing exposure than with AppSec engineers looking to automate pentesting and embedding security testing in CI/CD.

❌ Developer gap: Reports validate impact but don’t provide developer-ready fixes or workflow integration

Testing Approach

• Event-driven, ASM-centered testing with adversary-style validation. Effective for proving which exposures are exploitable in real time, but not systematic in uncovering hidden business logic vulnerabilities.

Org Size Fit

• Best suited for mid-to-large organizations with large, dynamic external attack surfaces. 
• Not optimized for engineering-led teams focused on continuous in-app business logic testing and developer remediation workflows.

Reviews

“Hadrian provides real-time visibility of risks that we would have to wait until a penetration test to discover. It was simple to set up and has become a daily part of our workflows.”

Burp Suite

Burp Suite by PortSwigger is one of the most established tools in web application security testing. Known as the go-to toolkit for penetration testers and bug bounty hunters, Burp combines a powerful intercepting proxy with an automated scanner. Burp AI provides AI-powered insights, automation, and efficiency improvements for security professionals and bug bounty hunters using Burp Suite Professional. While trusted across the industry, Burp remains primarily a manual-first platform: effective in expert hands, but not built for continuous coverage or systematic business logic testing.

Strengths

✅ Customizable testing: Highly customizable, ideal for advanced users needing fine-tuned pentesting capabilities

✅ Authentication support: Supports a wide range of authentication methods for complex login workflows

✅ Large ecosystem: Part of the Burp Suite ecosystem with a large user and plugin community

Limitations

❌ Manual expertise required: Delivers the best results only in expert hands, requiring significant tuning and manual effort.

❌ Modern API gap: Lacks built-in automation for GraphQL

❌ Business logic blind spot: Detection of vulnerabilities like BOLA, IDOR, or workflow bypasses requires human assistance

❌ Resource-intensive: Extensive scans can be slow and consume significant resources, limiting scalability.

Testing Approach

• Manual-first workflow with automated scanning add-on. Strong for security researchers needing control and exploit flexibility; weaker for systematic, evidence-driven automation at scale.

Org Size Fit

• Best for security researchers, bug bounty hunters, and AppSec teams with strong manual testing skills. 
• Less suited for organizations seeking automated pentest replacement, developer-ready remediation, and continuous coverage of complex applications.

Reviews

“If you're a web application pentester, you have to know the Burp Suite. The packet intercepting/modification feature is really important in any web application pentesting.”

Cobalt.io

Cobalt positions itself as a human-led, AI-powered pentesting platform, explicitly rejecting fully autonomous agentic AI approaches. Their model uses AI to accelerate pentesting workflows - from intelligent tester matching to streamlined reporting - while experienced human pentesters focus on finding sophisticated vulnerabilities. The platform combines access to a community of vetted pentesters with AI tools that handle repetitive tasks like report writing and data enrichment. Cobalt's AI models are trained on over a decade of real pentesting data, rather than synthetic datasets.

Strengths

✅ Human-led approach with AI augmentation - pentesters leveraging AI tools deliver actionable insights faster than traditional methods

✅ Start pentests in as little as 24 hours with on-demand access to expert talent

✅ Real-time collaboration - direct communication with pentesters via Slack and in-platform messaging

✅ Unlimited free retesting for fixed vulnerabilities

Limitations

❌Not fully automated - requires human pentesters, so can't achieve the same continuous testing speed as pure AI solutions

❌ Cobalt credits can be costly, making it difficult for organizations with large application portfolios

❌ Scheduling can sometimes take longer than expected, especially for retesting or specialized scopes

❌ Less suited for organizations seeking fully automated, CI/CD-native security testing without human dependency

Testing Approach

Human-led, AI-powered methodology where AI handles repetitive tasks while expert pentesters focus on complex vulnerabilities. Combines Pentest as a Service (PTaaS) with optional DAST scanning. AI/LLM testing follows OWASP methodologies, focusing on prompt injection, business logic, and application security. Platform emphasizes collaboration with direct pentester communication throughout the engagement.

Org Size Fit

Mid-to-large enterprises and regulated organizations that value human expertise and need compliance-ready pentesting (SOC 2, ISO, PCI-DSS).

Less ideal for startups or engineering-led teams needing continuous, fully automated testing integrated into CI/CD pipelines.

Reviews

"Cobalt provides an excellent balance of flexibility and expertise in penetration testing. I like how their platform makes it easy to track findings, communicate directly with testers, and manage retesting. The talent and professionalism of their pentesters stand out—they deliver actionable results, not just reports. The continuous visibility into progress and remediation guidance is a huge value add."

Practical approaches for integrating AI pentesting tools in your stack

So how can you make continuous pentesting with AI-powered tools a reality in your organization?

Before diving into continuous pentesting, it’s crucial to define your goals and boundaries for automation. Start by asking yourself:

• How much power are you willing to give the automated tool initially?
• Which applications should be prioritized for automated testing?
• How much of your budget are you hoping to reduce by automating?
• What does your tech and security stack look like?

Use case 1: AI-powered pentesting as part of DevSecOps practices

DevSecOps teams often run into the same challenge: as development velocity increases, security reviews become a major bottleneck. Every new release is expected to be secure, yet traditional DAST tools and manual pentesting simply can’t keep pace with rapid CI/CD pipelines.

“Now our developers are a lot more productive, but we’re also pushing twice as much or three times as much code into production. My team is not three times the size.” - Nick Semyonov, Director of IT & Security, PandaDoc

In this scenario, to implement automated AI pentesting, we start with an existing pipeline. When new code is pushed to production, it first enters the CI stage. There, it typically goes through several quality and security gates such as SCA, SAST, and secret detection.

Next, the code moves into CD, where it undergoes Dynamic Application Security Testing (DAST) scanning and a manual security review before being released to production.

With automated AI-driven pentesting, both the DAST stage and the manual review can be significantly streamlined or fully replaced. This accelerates the process by orders of magnitude while maintaining the same level of security and data quality.

Another important aspect of this strategy is that it can be fully automated using a CLI-based workflow. At Escape, we provide a CLI tool that allows teams to run security assessments anytime and anywhere, manage attack surfaces, and trigger automated pentests directly from within their pipelines.

Use case 2: AI-powered pentesting as part of Cloud Security

Cloud environments evolve constantly: new services are getting released, configurations change, and workloads shift. Traditional pentesting can’t keep up with this dynamic reality. Most organizations rely on periodic manual tests or static scans, which quickly become outdated and fail to reflect the real attack surface.

To address this, modern AI-powered pentesting workflows start by integrating directly with the organization’s existing security ecosystem rather than operating in isolation. These systems can connect to cloud security posture management tools (such as Wiz), import historical pentest reports, and pull data from vulnerability or bug-bounty platforms. This helps build a unified, consistent picture of risks across multiple sources.

AI-powered platforms can connect directly to cloud and IT environments to generate an accurate, real-time understanding of the attack surface. This is typically done through specialized discovery agents that continuously analyze cloud configurations, applications, exposed assets, identity relationships, and infrastructure changes.

You can see an example of how it can be done with Escape AI pentesting below, which was mentioned in the recent webinar:

Once this live attack-surface map is created, AI pentesting agents can automatically run precise, context-aware tests on any part of the environment. These tests can be triggered in real time (whenever new assets appear or configurations change) or scheduled to run on a weekly or monthly basis. The result is targeted, high-coverage pentesting that adapts to changes in the cloud as they happen.

Conclusion

The analysis across AI pentesting tools helping with automation highlights one truth: manual pentesting no longer scales with how software is built and shipped, but it requires a tool that can actually replace it efficiently.

The real measure of value isn’t whether the platform is there to help you to just check the compliance box. It’s whether a platform can actually replace human pentests and enhance its value. When you are evaluating a tool, you should ask yourself:

• Can the tool reliably find even complex business logic flaws that are usually only found through bug bounty or by manual pentesters?
• Can it deliver findings without humans needing to constantly recheck its validity and provide tailored ways to fix the issues, so engineers can fix without debate?
• Can results be tied directly to assets and owners, so security engineers know what matters most and who is responsible?

That’s where Escape sets itself apart.

Escape AI-powered pentesting solution was purpose-built to replace the weeks-long, high-cost pentest cycle. Escape continuously models how your applications behave, uncovers business logic flaws other tools miss, helps you to integrate complex exploits, and provides developer-ready fixes with full exploit paths.

The result: faster remediation, fewer blind spots, and pentesting that finally keeps pace with modern engineering.

If your team is ready to see how automated pentesting actually works in practice, book a demo with our product expert.

FAQ

What are the common challenges faced when using AI in penetration testing?

AI-driven pentesting promises huge scalability, but it comes with some real challenges. Tools may struggle when they don’t have up-to-date data about your infrastructure or attack surface, leading to missed issues or noisy output. Integrating AI pentesting into complex cloud environments or existing DevSecOps pipelines can also be tricky. Even when the AI finds something, interpreting results often still demands human expertise. That’s why a tool like Escape is valuable: it combines continuous discovery with business-logic–aware algorithms, helping reduce false positives and making sure the findings stay relevant as your architecture evolves.

Which AI pentesting tools are the most effective for automated penetration testing?

The most effective AI pentesting tools are those that go beyond simple scanning: they discover your full attack surface, understand your application’s logic, handle complex authentication and user flows, and automate tests continuously as code changes. Escape is a top example: it uses proprietary AI to detect business-logic flaws (like IDOR, BOLA, or complex access-control issues), works across APIs, SPAs, and distributed environments, and integrates into CI/CD pipelines for real-world, repeatable pentesting at scale.

How do AI pentesting tools identify security vulnerabilities?

Modern AI pentesting tools don’t just look for known vulnerability patterns. They reason about how your app behaves. They map your attack surface, simulate user sessions (with roles, sessions, and authentication), and run exploit-like sequences to test business logic, authentication flows, and interdependent parts of your infrastructure. Escape, for instance, uses an engine that models real application states and transitions, letting it uncover subtle logic flaws or multi-step attack chains that traditional scanners would miss.

What are the privacy and ethical considerations when using AI for pentesting?

When using AI for pentesting, it’s important to carefully control which systems and data the tools can access. Because they often analyze source code, cloud configs, or live APIs, there’s a risk of exposing sensitive data if permissions and scopes aren’t well defined. Tools like Escape help mitigate this by using secure workflows and focusing their discovery on authorized assets. Additionally, teams should maintain audit logs and ensure AI-driven tests don’t interfere with production workloads — balancing automation with responsibility.

What types of security threats can AI pentesting tools detect?

AI pentesting tools can find everything from classic vulnerabilities (like SQL injection, XSS, insecure configurations) to deep business-logic defects, broken access controls, insecure authorization (IDOR, BOLA), authentication bypasses, API misuse, and multi-step exploitation paths. Because they simulate real user behavior and adapt based on what they discover, tools like Escape can catch vulnerabilities that often slip through standard scanning or manual reviews — especially in complex, distributed, modern architectures.

What is the difference between DAST and AI pentesting?

Most of the DAST tools use a traditional approach: they treat the application as a black box, send generic test inputs, and look for known vulnerabilities. AI pentesting, however, as implemented, for example, by Escape, thinks more like a human hacker. It understands user flows, authentication, business logic, and can adapt tests based on application state. That means AI pentesting can uncover deeper issues (logic flaws, chained exploits, broken authorization) that traditional DAST tools alone often miss, and it can run continuously as your app changes.

How does Escape compare to the top AI Pentesting Tools?

Escape stands out by combining continuous attack-surface discovery, business-logic–aware testing, and developer-friendly output. Unlike simpler scanners or manual-first tools, Escape’s AI engine simulates real user interactions (roles, sessions, complex auth), detects logic-based vulnerabilities, provides proof-of-exploit and suggested fixes, and integrates into CI/CD for ongoing testing. That makes Escape ideal for fast-moving teams or organizations with many services and frequent deployments — offering near-continuous pentesting that keeps pace with modern development.

💡 Check out more relevant articles below:

• The Future of Pentesting: Can AI Replace Human Expertise? ⎥ Jyoti Raval
• Top automated pentesting tools in 2026
• How to automate your penetration testing
• Best DAST tools in 2025
• Top 10 Vulnerability scanning tools 2025
• Escape Research: Escape's proprietary Business Logic Security Testing algorithm