Gno.land, a Go-based smart contract platform developed by NewTendermint, has introduced Dora, an autonomous AI agentic security system designed to continuously examine the platform’s codebase and smart contracts, known as realms.
Unlike conventional security tools that flag potentially vulnerable code for later investigation, Dora is designed to establish whether a suspected weakness can actually be exploited. The system reproduces each suspected exploit against a live Gno.land node before presenting the finding for human review, aiming to reduce false positives and identify actionable vulnerabilities earlier.
The launch reflects a broader shift among blockchain projects toward using agentic artificial intelligence for continuous security monitoring instead of relying primarily on periodic audits or vulnerability reports submitted through bug bounty programs.
Gno.land was founded by Jae Kwon, co-creator of Cosmos and Tendermint, and uses Gno, an interpreted and deterministic variation of Go. The platform is designed to allow developers to create readable and composable smart contracts while requiring source code to remain publicly available on-chain. That architecture is intended to improve transparency and make contracts easier for developers and security researchers to inspect.
NewTendermint, which is led by Kwon, develops infrastructure for decentralized systems, including Tendermint2 and Gno.land. The company serves as a core maintainer of the Gno.land protocol and GnoVM, supporting open-source infrastructure intended for permissionless applications.
Seven-agent process targets exploitable vulnerabilities
The need for continuous verification has increased as artificial intelligence has made it easier to identify suspicious code. While AI-assisted tools can quickly locate potentially problematic sections of software, determining whether those issues represent genuine and exploitable vulnerabilities remains more difficult. The same capabilities are increasingly accessible to both security teams and potential attackers.
For blockchain networks, the consequences of an undetected software defect can be significant. A panic in a critical component could interfere with consensus, nondeterministic behavior could cause validators to diverge, and arithmetic errors could potentially affect asset transfers.
Dora addresses this challenge through a seven-stage agent workflow covering reconnaissance, vulnerability planning, deduplication, verification, adversarial review, patch development and final fix validation.
The reconnaissance agent maps the relevant code, while the planning agent develops potential vulnerability scenarios. A deduplication stage removes issues that are already known. The verifier then creates and executes a live test against an actual Gno.land node to determine whether the suspected weakness can be reproduced.
A separate reviewer evaluates the result from an adversarial perspective. If the issue remains valid, a fixer prepares a patch, followed by another review to determine whether the proposed correction resolves the vulnerability without introducing another problem. Only findings that pass the complete process are forwarded to human reviewers.
Gno.land expands AI-focused development tools
Dora forms part of Gno.land’s broader effort to integrate artificial intelligence into both development and security processes. The platform has also released gnomcp, an open-source server intended to connect AI coding agents such as Claude Code and Cursor directly with Gno.land.
Through that integration, AI agents can interact with Gno.land to read, write and deploy realms using development workflows familiar to software engineers. The platform’s use of human-readable Go-based smart contracts is also intended to make its code more accessible to AI systems compared with architectures where application logic is represented primarily as compiled bytecode.
Dora is currently an internal security tool, and Gno.land has not yet completed a comprehensive audit of its entire codebase, although targeted testing has already generated reproducible findings with relatively low noise.
The company plans to expand Dora’s coverage and publish additional results as the system is deployed more broadly. The initiative could provide Gno.land with a continuous security layer that complements traditional audits and human-led vulnerability research while allowing confirmed issues to be addressed before they are discovered externally.
