Security Research

Security Research & Technical Findings

Xenqube's security research covers the threat landscape across Web3 protocols, on-chain AI systems, post-quantum cryptography, and enterprise security operations. Our findings inform both internal engineering practice and published technical guidance for the wider security community.

Web3 security Adversarial AI defense Post-quantum cryptography Smart contract vulnerabilities

Research domains

Smart contract security

Analysis of common and emerging vulnerability patterns in production smart contracts: reentrancy variants, oracle manipulation, flash loan attack surfaces, governance attack vectors, and upgrade proxy risks. Research informs our audit-readiness methodology and development practices.

Adversarial AI security

Research on attack surfaces in AI systems deployed in production: prompt injection, model extraction, data poisoning in training pipelines, adversarial example generation, and guardrail bypass techniques for on-chain and off-chain AI agents.

Post-quantum cryptography

Implementation research on NIST-standardised PQC algorithms: performance benchmarking of CRYSTALS-Kyber and Dilithium in constrained environments, hybrid migration patterns, and cryptographic agility design for long-lived systems.

Web3 threat intelligence

Analysis of on-chain exploit patterns, DeFi protocol attack post-mortems, cross-chain bridge vulnerabilities, and governance manipulation techniques. Used to inform proactive security controls in protocol development engagements.

How research informs engineering

Xenqube's security research is applied directly to the systems we build and secure. Findings from vulnerability analysis are incorporated into the smart contract development methodology, threat modelling frameworks, and security operations runbooks used across all client engagements.

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