Cracking the Code on Offensive Security With AI ft XBOW CEO and GitHub Copilot Creator Oege de Moor

Market Context & Disruption Thesis

• AI code generation is increasing vulnerability density as models trained on public source code (containing existing flaws) produce more insecure code.
• Attackers are simultaneously leveraging AI to enhance the effectiveness of malicious campaigns.
• This dual threat (more vulnerable code + smarter attackers) necessitates the automation of offensive security, a niche previously limited by high costs and slow turnaround.

Expo Product Performance & Benchmarks

• Initial product testing on industry-standard benchmarks (sourced from PortSwigger and Pentastellan) yielded a 75% success rate.
• Skepticism regarding the 75% score arose due to concerns that benchmarks were present in the training data; consequently, Expo created a proprietary set of original benchmarks not in any training set.
• On these new, proprietary benchmarks, Expo achieved an 85% success rate.
• In a comparative test against five professional penetration testers (including a top-tier expert securing multi-billion dollar hedge funds) on 104 challenges:
  • The top human expert scored the same 85% as the AI.
  • The human expert required 40 hours to complete the test.
  • The Expo AI system completed the same test in 28 minutes.

Real-World Vulnerability Discovery

• During early design partner engagement, the system autonomously discovered a critical vulnerability in a major financial institution (a client with massive security budgets and constant human testing).
• The specific flaw involved an insecure signing algorithm in PHP source code that allowed unauthorized access to a secondary host.
• The AI subsequently identified a vulnerability in JavaScript allowing the download of password files.
• The discovery process demonstrated "human-like" creative exploration, such as iteratively testing parameters and chaining discovered attacks across different endpoints.

Operational Model & "Service as a Software" (SaaS)

• Expo automates the entire simulated attack process, enabling continuous security testing rather than the traditional annual or bi-annual manual penetration test.
• The typical cost of a manual penetration test is approximately $18,000, limiting frequency; Expo aims to replace this with a continuous, automated model.
• Pricing Strategy:
  • Base tier: Subscription license based on engineering team size (similar to GitHub Advanced Security).
  • Add-on tier: "Attack hours" model allowing customers to pay for increased compute/inference time for more exhaustive testing.
• Guardrails: The system utilizes cloud-only deployment to enforce scope restrictions, requiring customers to prove legitimate ownership of targets to prevent unauthorized attacks.
• Validation: All AI findings are automatically validated for reproducibility and correctness before human review.

Technical Architecture & Data Strategy

• Core Technology: Proprietary security domain knowledge and tooling built on top of foundation models, rather than relying solely on model capabilities.
• Key Components:
  • Custom LLM-driven web browser for interacting with targets.
  • A curriculum of benchmarks used to train and evaluate the model's progression.
  • "Brilliant teenager" guardrails to prevent destructive actions (e.g., dropping database tables).
  • Automated attack surface discovery and prioritization tools.
• Data Generation: The team mines open-source repositories (e.g., Docker Hub) to generate new training data by observing successful attack patterns across hundreds of attempts.
• Autonomy: Recent testing showed the AI could identify vulnerabilities (e.g., data leakage) without specific instructions, relying on its contextual understanding of the application's purpose.

Founder Background & Strategic Vision

• Uge Demore (CEO/Founder) is a former Oxford computer science professor and the creator of GitHub Copilot; he previously founded Semmel (acquired by GitHub).
• Timeline: The concept of autonomous agents fixing bugs was theorized in a December 2020 memo during the early Co-pilot development phase.
• Competitive Landscape: Demore notes that while coding assistants are crowded, the opportunity lies in targeting non-professional coders (e.g., Replit model) and high-impact security markets.
• Strategic Goal: The primary driver is to deploy defensive AI faster than adversarial state actors or bad-faith entities can weaponize it.

Future Roadmap & Outlook

• Short-term (Next Few Months): Continued operation with human supervision for early design partners to refine guardrails.
• Medium-term (By Next Summer): Full deployment without supervision, aiming to significantly transform web security standards.
• Platform Expansion: Plans to demonstrate capabilities on platforms like HackerOne and through open-source projects.
• Broader Market Impact: Anticipated disruption in customer support, social media moderation, and long-term expansion into health and biology.
• Long-term Vision: Demore predicts a shift where "English + diagrams" becomes the primary coding language, with AI handling the implementation details, moving up the abstraction ladder from machine language to natural language.

Lightning Round Insights

• Favorite Startups: Suno (AI music generation) and Harmonic (AI mathematical reasoning).
• Advice for Founders: Focus on a single objective and prioritize speed of execution above all else.
• Optimistic Forecast: The next decade will be defined by AI expanding global health outcomes in biology and healthcare.