Ripple Deploys AI Red Team on XRP Ledger, Here's What It Found

Ripple is integrating artificial intelligence across the full development lifecycle of the XRP Ledger (XRPL), including automated code scanning, adversarial testing, and a dedicated AI-assisted red team. The effort is already producing results: the red team has identified more than 10 bugs, with lower-severity issues disclosed publicly so far.

Why Is Ripple Overhauling XRP Ledger Security Now?

The XRP Ledger has been running continuously since 2012. In that time, it has processed more than 100 million ledger entries and facilitated over 3 billion transactions. That track record is significant, but it also comes with a practical consequence: a codebase that reflects more than a decade of engineering decisions, some of which predate modern security tooling.

"Design decisions made in earlier phases of the network, assumptions that held at smaller scale, and patterns that predate modern tooling collectively shape how the system operates today,” Ripple noted in a recent blog post.

The company links the timing of this overhaul to the XRPL's expanding role. The network now supports institutional payments, real-world asset tokenization, and financial infrastructure projects such as the Monetary Authority of Singapore's BLOOM initiative, a central bank-backed program exploring digital money and payments. As the workloads become more complex and the stakes higher, Ripple argues that older testing approaches alone are no longer sufficient.

The Role Of AI In Modern Security Testing

AI is not new to software security, but its application to blockchain protocols has accelerated. Machine learning tools can systematically explore large codebases, surface edge cases, and simulate attacker behavior at a scale that manual review cannot match.

A relevant data point: during a two-week experiment, Anthropic's Claude Opus 4.6 model identified 22 vulnerabilities in the Firefox browser, 14 of which were classified as highly severe. That kind of result has prompted blockchain developers across the industry to take AI-assisted security more seriously.

Ripple's position is that malicious actors are already using similar tools to find vulnerabilities, which requires a symmetrical response from the development side.

What Does Ripple's AI Security Strategy Actually Include?

The strategy is built around six pillars, covering everything from how code is written to how changes are approved for the live network.

The core technical components are:

• AI-assisted code scanning on every pull request (PR): Every proposed code change is reviewed using adversarial scanning tools before it is merged, catching issues earlier in the process.
• Automated fuzzing and adversarial testing: Ripple runs fuzzing, which means feeding unexpected or malformed inputs to the system to see how it responds, guided by explicit threat models rather than random inputs.
• Threat modeling and attack surface mapping: New and existing features are analyzed for how they interact with each other, not just how they behave in isolation.
• Edge case simulation: AI tools generate stress scenarios that would be impractical to construct manually, particularly at the boundaries where older code meets newer functionality.

The AI-Assisted Red Team

A red team in security is a group whose job is to think and act like an attacker. Ripple has established a dedicated AI-assisted red team focused specifically on the XRPL codebase. The team examines how features interact under real-world conditions rather than testing each feature in isolation, which is where long-lived systems tend to be most fragile.

The red team has already found more than 10 bugs. Ripple says all identified issues are being prioritized and fixed, with more significant findings handled through coordinated disclosure processes.

How Is Ripple Addressing Structural Code Problems?

Beyond active testing, Ripple is working to modernize the underlying codebase itself. This addresses a category of problems that testing alone cannot fully solve.

In long-lived systems, bugs often stem from structural issues rather than isolated mistakes. Ripple has identified several of these in the XRPL:

• Limited type safety, meaning the code does not always enforce strict rules about what kind of data a function can accept or return.
• Inconsistent interaction patterns between features that have been added at different points over the network's history.
• Insufficient invariant enforcement, where assumptions about how the system should behave are not formally checked by the code itself.
• Undocumented or unenforced assumptions that developers rely on implicitly but the system does not verify.

Fixing these issues makes the system more predictable and easier to reason about, reducing the likelihood of bugs arising from unexpected interactions.

What Changes For XRPL Amendments?

Amendments are the mechanism through which protocol-level changes are activated on the XRP Ledger. They require validator consensus before taking effect.

Ripple is raising the bar for how amendments are evaluated before activation. Going forward, significant protocol changes will require multiple independent security audits, expanded bug bounty programs to incentivize external researchers, and adversarial testing through attackathons, which are structured events where participants actively try to break new features before they go live.

Ripple says it will define and publish explicit security readiness criteria in collaboration with the XRPL Foundation, establishing clear thresholds for testing, review, and risk assessment that amendments must meet before being enabled on the network.

What Comes Next For The XRP Ledger?

Ripple confirmed that the next XRPL release will be dedicated entirely to bug fixes and code improvements, with no new features included. This signals a deliberate pause on feature development to focus on foundation work.

The company also plans to deepen collaboration with external partners including XRPL Commons, the XRPL Foundation, independent security researchers, validator operators, and external security firms. Distributing security efforts across multiple organizations with different perspectives is a standard practice in high-stakes infrastructure, and one that Ripple is now formalizing for the XRPL.

Security disclosures, published findings, and lessons learned will be shared openly with the broader community as part of an explicit transparency commitment.

Conclusion

Ripple is embedding AI into every stage of XRP Ledger development, from the review of individual code changes to full-scale adversarial simulation of the live network. 

The red team has already found more than 10 bugs, the next XRPL release will contain no new features, and new security criteria for amendments are being developed with the XRPL Foundation. The effort is a direct response to the network's expanded role in institutional payments and asset tokenization, where the tolerance for infrastructure failure is close to zero.

Resources

1. Blog article by Ripple: Strengthening XRP Ledger Security with AI For Next Phase of Growth

2. Report by Tech In Asia: Ripple adds AI security checks across XRP Ledger development

3. Report by CoinDesk: Ripple turns to AI to stress-test the XRP Ledger as institutional use cases scale