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// Select a tool above to run security analysis.
The honest, current picture of penetration testing is a genuine hybrid moment — AI-driven tools now handle breadth, speed, and pattern-matched vulnerability classes with real, measurable effectiveness, while the most damaging real-world vulnerabilities continue to be the ones requiring genuine reasoning: a role check that's missing only after a very specific sequence of account state changes, a dry-run endpoint that redacts a secret in one field but leaks it in another, an OIDC flow that only breaks when one client exchanges another client's authorization code. These are reasoning problems, not template problems, and a testing approach that leans entirely on automated pattern-matching will miss them.
The second problem is testing frequency and scope drift. A large share of organizations still test only once annually, which means a system that's changed substantially since its last assessment — new features, new integrations, new dependencies — is operating with a security picture that's meaningfully out of date for most of the year.
The third problem, increasingly relevant, is that a growing share of applications now include genuine AI components — agents, LLM integrations — introducing an entirely new attack surface category that conventional web, API, and mobile testing methodologies weren't built to cover, requiring testing specifically designed around planning-layer manipulation, tool-call abuse, and memory poisoning.
We run a genuinely hybrid methodology — AI-accelerated tools handle the breadth work efficiently (asset discovery, pattern-matched vulnerability classes, initial reconnaissance across a large attack surface), freeing senior human testers to spend their time on exactly the reasoning-heavy work that actually finds the vulnerabilities that matter most: business logic flaws, authorization edge cases, and multi-step exploit chains that require understanding what the application is supposed to do, not just what patterns it superficially matches.
For web, API, and mobile applications, every finding is manually validated before it reaches a report — we don't deliver raw, unverified tool output dressed up as a professional assessment. And for applications with genuine AI components, we extend our methodology to cover the layers a conventional pentest doesn't reach: the planning layer, the tool-call layer, and the memory layer, testing specifically for prompt injection, tool description poisoning, and cross-session context leakage.
System Features
01.AI-Accelerated Breadth Coverage
Automated tools handling reconnaissance, asset discovery, and pattern-matched vulnerability classes at a scale and speed no manual process alone could match.
02.Human-Led Business Logic Testing
Senior testers focused specifically on the reasoning-heavy vulnerability classes — authorization edge cases, multi-step exploit chains, logic flaws — that automated pattern-matching reliably misses.
03.Manually Validated Findings
Every reported vulnerability manually confirmed and demonstrated before it reaches your report, not raw automated tool output.
04.Cross-Surface Testing (Web, API, Mobile)
Consistent, expert testing methodology across your entire application surface, not siloed assessments that miss cross-surface attack chains.
05.AI Agent & LLM Application Testing
Specialized testing methodology for applications with genuine agent or LLM components, covering planning, tool-call, and memory attack surfaces.
Four-Layer AI Application Attack Surface
For applications with genuine AI agent or LLM components, we test four distinct layers that a conventional web application simply doesn't have. The planning layer, where the system interprets natural language and decides on actions, is tested for indirect prompt injection — embedding adversarial instructions in documents or data the agent retrieves and processes, then verifying whether the agent follows an instruction it should have treated as untrusted data rather than a command. The tool layer, where the agent calls external systems with real privileges, is tested for whether attacker-controlled input can produce attacker-controlled tool arguments, and whether tool combinations create privilege escalation paths that exceed what either tool was individually meant to allow.
The memory layer, where context persists across sessions, is tested for cross-session context leakage and memory poisoning — whether one user's session can influence or leak into another's. And because AI systems introduce genuine non-determinism, we test across repeated runs rather than a single pass, since a vulnerability that appears only some percentage of the time is still a real, exploitable vulnerability, and single-pass testing would miss it entirely. Across real-world engagements against production AI agent systems, a meaningful majority have shown vulnerability to at least one of these categories — which is precisely why this is treated as a distinct, necessary testing discipline rather than an afterthought bolted onto conventional application testing.
// Real-World Use Cases
- >Company needing a comprehensive penetration test across web, API, and mobile before a major launch or funding round
- >Business running AI agents or LLM-powered features needing testing methodology built specifically for that attack surface
- >Organization needing more frequent testing than an annual assessment to keep pace with continuous deployment
- >Company preparing for a security-conscious enterprise sale or compliance requirement needing a credible, validated pentest report
- >Business that's experienced a security incident needing a thorough reassessment of its actual attack surface
// Measurable Business Impact
- ✔Comprehensive attack surface coverage at a speed and cost that purely manual testing can't match
- ✔Genuine detection of the reasoning-heavy vulnerability classes that cause the most severe real-world damage
- ✔A trustworthy, manually validated report your engineering team can act on immediately without chasing false positives
- ✔Real security coverage for AI agent and LLM components, an attack surface most testing providers still don't address
- ✔Support for more frequent testing cadence, closing the risk exposure gap left by once-a-year assessment cycles
Frequently Asked Questions
Get the breadth of automation and the judgment of a real tester
AI accelerates coverage. Human reasoning finds what actually matters.
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