LFDT Korea

A learning group for zero knowledge and SNARK application development.

The tokenisation of regulated real-world assets has led to multiple institutional implementations of the same financial instruments, each operating on its own infrastructure with few shared patterns for how those infrastructures interoperate. As a result, tokenised assets remain confined to the environment they were issued in, accessible only to counterparties already inside the same institutional perimeter.

Join us for a discussion about the new VeltoraCore lab that is building a pair-coin, system-agnostic optimization engine that jointly coordinates user execution and LP participation in a single feasibility framework. A lightweight Proof-of-Optimization (PoO) mechanism cryptographically commits optimized batch outcomes to external system states, enabling verifiable cross-system settlement. Simulations on the ETH–USD pair show superior retained execution and materially lower user fees relative to benchmarks, while delivering liquidity-provider returns higher with reduced volatility.

This is a great opportunity to talk to the developers about what they're building and to learn how to get involved if this sounds interesting.

AI agents are entering production with long-lived API keys in their environment, broader access than any task needs, and no answer to the questions: Which agent performed this action? Under whose authority?

A single prompt injection, leaked credential, or runaway agent can quickly become a security incident. Security reviews delay deployments, and auditors are often left without sufficient evidence to understand what happened.

Existing security infrastructure was not designed for autonomous AI agents. Identity providers register agents but do not authorize every action, secret managers distribute credentials to workloads, and API gateways route requests without making authorization decisions.

This talk introduces Caracal, an open source AI authorization system currently being developed through the LFDT LFX Mentorship program and supported by GitHub, Microsoft, and Vercel. We'll discuss what authority means for AI agents, why authorization must move beyond credentials, and how we designed Caracal's architecture to provide policy-based authorization, attribution, revocation, and auditability for agent actions.

The session will cover:

• Why traditional API key and identity models are insufficient for AI agents
• What authority means in autonomous AI systems
• The architectural design behind Caracal
• Practical approaches to securing AI agents in production

The meetup will be presented by the Caracal team, Ryan Madhuwala and Shubham Sharma.

Our LFX mentees, Mohammad Ali Haider and Ashutosh Singh, will also present their work on the project. If you're interested in AI security and secure agent infrastructure, we'd be happy to have you join us.