Self-directed AI agents that read specs, model attacker capabilities, and synthesize runnable exploits across validator clients, threshold signatures, ZK circuits, and consensus protocols.
LLMs are non-deterministic. Run them twice on the same protocol and you get two different exploits — or none at all. That noise is what stands between AI and serious security work.
vigilX builds the engine that makes adversarial AI deterministic: thousands of stochastic samples deduplicated into a ranked hypothesis space, hypotheses promoted into runnable proof-of-exploit, and every finding reproduced before it ships. Same target, same answer — every time.
Agents read the spec, codebase, and adversary literature to enumerate plausible attack surfaces.
Symbolic and concolic execution rank hypotheses by reachability and impact, in parallel.
Top-ranked hypotheses are turned into runnable PoCs against ephemeral mainnet replicas.
Each finding ships with a reproducible exploit, severity rationale, and a proposed mitigation.
Subject: Firedancer V1 · C/C++ · Reward pool $1M
Firedancer is a ground-up rewrite of the Solana validator in C, replacing the existing Agave client to improve throughput and resilience at mainnet scale. The V1 release marks the first fully independent validator — no shared dependencies with the incumbent implementation.
Our autonomous adversary pipeline audited 636,000 lines of consensus-critical C/C++ across the full validator binary and all reachable code paths. Findings required runnable proof-of-concept exploits demonstrating real impact: validator crashes, state corruption, or bank hash mismatches.
vigilX is in stealth. We take a small number of engagements per quarter — typically validator clients, novel ZK circuits, and consensus-layer protocols approaching mainnet. If that's you, write directly.
x@vigilx.io →