AI vs Human Hackers: Who Prevails in 2026 Pen Testing?

AI Agents vs. Humans: Web Hacking in 2026

Wiz Research and Irregular, an AI security lab, collaborated to compare AI agents and human hackers in web vulnerability exploitation.

Methodology

Ten lab environments were created, each simulating real-world security issues. The challenges used a Capture the Flag (CTF) setup, tasking AI agents to find and exploit vulnerabilities to retrieve a unique "flag." The AI models were equipped with standard security testing tools via Irregular’s proprietary agentic harness.

The challenges included:

• VibeCodeApp: Authentication Bypass, inspired by a real-world hack of a vibecoding platform.
• Nagli Airlines: Exposed API Documentation, based on a real-world hack of a major airline.
• DeepLeak: Exposed Database, based on a real-world hack of DeepSeek.
• Shark: Open Directory, based on a real-world hack of a major domain registrar.
• Logistics XSS: Stored XSS, based on a real-world hack of a logistics company.
• Fintech S3: S3 Bucket Takeover, based on a real-world hack of a fintech company.
• Content SSRF: AWS IMDS SSRF, based on a real-world hack of a gaming company.
• GitHub Secrets: Exposed Secrets in Repos, based on a real-world hack of a major CRM.
• Bank Actuator: SpringBoot Actuator Heapdump Leak, based on a real-world hack of a major bank.
• Router Resellers: Session Logic Flaw, based on a real-world hack of a routers company.

The AI agents were instructed to explore websites and exploit vulnerabilities to retrieve unique "flags". An experienced penetration tester solved each challenge beforehand to ensure fairness. The flags served as definitive proof of successful exploitation.

Results

The AI models successfully solved 9 out of the 10 challenges. The cost per success was calculated, accounting for success rates across multiple runs, including failed attempts, using the expected cost per success metric.

Observations

Low Cost for Solving Challenges

The cost per success metric was used to evaluate success, relevant in cyber threat models where attackers can retry without consequence. This metric doesn't include the entire cost of a cyber operation, such as agent development or human involvement. In challenges like Shark, Router Resellers, and Content SSRF, the models succeeded in 30% to 60% of runs. The stochastic nature of LLMs means that running them multiple times is likely to reveal the flag without raising monitoring alarms.

Broad Scope Degraded Performance

When AI was given a wide scope without a specific target, performance decreased. The cost of finding flags increased by a factor of 2-2.5 compared to single CTFs. Without a defined entry point, the agents spread their efforts haphazardly. The human tester narrowed focus as they found interesting signals; the AI agents didn't prioritize in the same way.

AI is Adept at Multi-step Reasoning

The VibeCodeApp challenge required bypassing authentication in a web application. Gemini 2.5 Pro discovered the documentation, found an app creation endpoint, and used the token to access the protected /chat endpoint in 23 steps. Other models achieved similar results.

Pattern Recognition is Fast

In the Bank Actuator challenge, an AI agent identified the Spring Boot framework by analyzing the timestamp format and structure of a generic 404 error message. It then targeted /actuator/heapdump and retrieved the flag. AI quickly identifies known vulnerability patterns.

Limitations of AI: Using Professional Tools and Creativity

In the Shark challenge, AI agents didn't use standard fuzzing tools, instead implementing their own scripts and word lists. If the wordlist didn't include "uploads," they didn't solve the challenge. The GitHub Secrets challenge, which required searching public repository history, was also not solved. The AI agents didn't consider accessing this public data source.

Boundary Testing

One AI agent explored its sandbox environment and found an open port (3306) connected to a MySQL server containing information about the running agents. The AI found the open port and accessed the database, which included the flag. This "reward hacking" highlights the potential for AI to test boundaries and look for unconventional paths.

Real-World Case Study

AI agents were used to investigate the root exposure that led to an actual security incident.

The Alert - Anomalous API Call

Wiz Defend flagged an anomaly: a Linux EC2 instance executed AWS Bedrock API calls with a macOS user-agent. The source IP was new to the organization, the instance had IMDSv1 enabled, and a public-facing IP. Accessing the public IP showed only a blank nginx 404 page.

Finding the Root Cause - AI Approach

An AI security agent was provided with ~5 directories discovered from initial fuzzing. The agent made approximately 500 tool calls over an hour, including SSRF payloads, .NET deserialization gadget chains, parameter fuzzing, type confusion tests, and callback server deployment. No successful exploitation occurred.

Finding the Root Cause - Human Approach

The human investigator expanded the search space by running comprehensive enumeration with a 350,000-path wordlist. This revealed /rabbitmq/, which led to the RabbitMQ Management Interface being exposed to the internet. Default credentials (guest:guest) provided full access.

Attack Chain Reconstructed

The attacker discovered /rabbitmq/, logged in with default credentials, read queue messages containing AWS credentials, and used them from their personal macOS device, triggering the Wiz Defend alert.

Analysis of the Differences

The AI worked thoroughly within the given search space, while the human expanded the search space. The AI was sensitive to the initial conditions and limited its scope. The AI ran for ~1 hour and made ~500 tool calls without finding the issue, while the human found it in ~5 minutes with broader enumeration.

Takeaways

• AI agents can perform offensive security, solving 9 out of 10 challenges.
• Per-attempt costs are low, favoring targeted exploitation.
• Testable objectives are important to reduce false positives.
• LLMs are familiar with cybersecurity methods and known attacks.
• AI agents excel at pattern matching.
• Challenges requiring broad enumeration or strategic pivots are harder for AI agents.
• Agents struggle to prioritize depth over breadth.
• AI iterates; humans pivot to new strategies.
• Framing the problem correctly is crucial; human judgment remains important.

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