The Security Landscape in 2026
- 97% of organisations are considering AI adoption in penetration testing
- 80x faster — AI pen testing vs traditional manual assessments
- 80% reduction in remediation time with AI-powered vulnerability detection
- 3.9x improvement over manual red teaming in vulnerability discovery
What is AI Penetration Testing?
AI penetration testing uses autonomous agents—not just scripts—to simulate real-world cyberattacks against your systems. Unlike traditional pen testing, where a human analyst manually probes for weaknesses over days or weeks, AI pen testing deploys intelligent agents that reason, adapt, and attack continuously.
These agents use Large Action Models (LAMs) and ReAct frameworks (Reasoning + Acting) to think through attack strategies, chain exploits together, and find vulnerabilities that automated scanners miss—including business logic flaws, authentication bypasses, and privilege escalation paths.
The Three Eras of Pen Testing
Manual Era (1995–2015)
The artisan approach
Expert hackers manually tested systems. Thorough but slow, expensive, and completely dependent on individual skill. A single assessment could take weeks and cost tens of thousands.
Automation Era (2015–2024)
DAST scanners and automated tools
Tools like Burp Suite and OWASP ZAP automated scanning. Faster, but they ran the same checks every time—good for known vulnerabilities, blind to business logic flaws and novel attack vectors.
Agentic Era (2025–Present)
Where we are now
AI agents that reason about your specific application, generate custom exploits, chain attack sequences, and adapt their approach based on what they find. They don't just scan—they think.
What AI Pen Testing Actually Covers
Modern AI pen testing goes far beyond running Nmap and checking for open ports. Here's what a thorough engagement includes:
Web Application Testing
OWASP Top 10 vulnerabilities, injection attacks, broken access control, session management flaws
API Security
Authentication bypasses, rate limiting, data exposure, broken object-level authorization (BOLA)
Cloud Infrastructure
Misconfigured S3 buckets, IAM privilege escalation, container escape, serverless function vulnerabilities
Network Penetration
Internal/external network testing, lateral movement simulation, service enumeration, credential stuffing
AI/LLM Security
Prompt injection, jailbreaks, data leakage, hallucination exploitation, model poisoning vectors
Business Logic
Price manipulation, workflow bypass, race conditions, privilege escalation through legitimate features
OWASP Top 10 for AI (2025/2026)
If your business uses AI—chatbots, recommendation engines, automated workflows—you need to think about AI-specific security. OWASP has published three critical frameworks:
OWASP Top 10 for LLM Applications (2025)
The definitive list of security risks for applications built on large language models:
- Prompt Injection — Attackers manipulate LLM behaviour through crafted inputs
- Sensitive Information Disclosure — Models leak training data or user information
- Supply Chain Vulnerabilities — Compromised model weights, plugins, or training data
- Excessive Agency — LLMs granted too many permissions or actions
- Insecure Output Handling — Failing to sanitise LLM-generated content
OWASP Top 10 for Agentic AI (2026)
Brand new framework for autonomous AI agents that plan and execute:
- Uncontrolled Autonomy — Agents making decisions without proper oversight
- Tool Misuse — Agents using integrated tools in unintended ways
- Memory Poisoning — Corrupting agent memory/context to alter behaviour
- Cascading Hallucination — Multi-agent systems amplifying false information
- Identity Spoofing — Agents impersonating other agents or users
AI Pen Testing vs Traditional: Side by Side
| Factor | Traditional Pen Test | AI Pen Test |
|---|---|---|
| Speed | Days to weeks | Hours |
| Coverage | Limited by analyst time | Hundreds of agents in parallel |
| Business Logic | Strong (human intuition) | Improving rapidly (agentic reasoning) |
| Frequency | Quarterly or annual | Continuous / on every deploy |
| Cost | $10K–$100K+ per engagement | Fraction of the cost, ongoing |
| Reporting | PDF report weeks later | Real-time dashboards + remediation |
| Exploit Validation | Manual proof-of-concept | Automated PoC generation |
Why Every Business Needs This Now
You don't need to be a Fortune 500 company to be a target. Attackers use the same AI tools to automate their attacks—scanning thousands of small and mid-size businesses simultaneously. If your defences haven't kept pace, you're the low-hanging fruit.
The Risk is Real
- 43% of cyberattacks target small businesses
- $4.88M — average cost of a data breach in 2024
- 60% of small businesses close within 6 months of a major breach
- Attackers now use AI to automate reconnaissance at scale
Our Approach: Human-Led, AI-Powered
At AI Makers, we combine autonomous AI agents with human expertise. Our agents do the heavy lifting—scanning, fuzzing, chaining exploits—while our team validates findings, eliminates false positives, and provides actionable remediation guidance you can actually follow.
Our AI Pen Testing Process
Scoping & Reconnaissance
Define targets, map attack surface, identify technologies and entry points.
Automated Agent Deployment
Deploy AI agents to scan APIs, web apps, cloud infrastructure, and networks in parallel.
Exploit Discovery & Chaining
Agents generate and test custom exploits, chain vulnerabilities, and simulate real attack paths.
Human Validation
Expert review of all findings. Eliminate false positives. Assess real-world impact and risk severity.
Reporting & Remediation
Comprehensive report with proof-of-concept exploits, priority ranking, and step-by-step fix guidance.
Retest & Continuous Monitoring
Verify fixes are effective. Set up continuous monitoring to catch regressions and new threats.
What You Get
Find Out What Attackers Already Know
Book a free security assessment call. We'll discuss your current setup, identify your highest-risk areas, and show you exactly how AI pen testing can protect your business.