Euler Labs

“With FailSafe, security isn't point-in-time. Their agentic security systems constantly scan for vulnerabilities evolving across contracts and infrastructure, allowing issues to be identified and acted on quickly.”

Kasper Pawlowski · CTO, Euler Labs
Megapot

“FailSafe's SWARM caught vulnerabilities that other AI security tools missed entirely. Their agentic approach found what traditional static analysis and competing AI reviewers couldn't.”

Brian · Founding Protocol Engineer, Megapot

Attackers are leveraging AI to probe you constantly.

01

The Threat

SWARM finds and fixes vulnerabilities before attackers exploit them.

02

The Solution

Built by security researchers. Battle-tested in production.

03

Built Different

01
02
03

Parallel Multi-Agent System

Claude
ChatGPT
Gemini
Grok
Claude
Gemini
ChatGPT
Grok
Claude
ChatGPT
Gemini
Grok
Claude
Gemini
ChatGPT
Grok
ChatGPT
Grok
Claude
Gemini
Grok
Claude
Gemini
ChatGPT
ChatGPT
Grok
Claude
Gemini
Grok
Claude
Gemini
ChatGPT
Gemini
Claude
Grok
ChatGPT
Gemini
Grok
Claude
ChatGPT
Gemini
Claude
Grok
ChatGPT
Gemini
Grok
Claude
ChatGPT
Grok
Gemini
ChatGPT
Claude
ChatGPT
Claude
Grok
Gemini
Grok
Gemini
ChatGPT
Claude
ChatGPT
Claude
Grok
Gemini

Machine Speed Vulnerability Detection

Weeks
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Hours
Weeks
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Hours

Compliance-Ready Reporting

ISO 27001
NIST
PCI DSS
OWASP
GDPR
MAS
ISO 27001
NIST
PCI DSS
OWASP
GDPR
MAS
SOC 2ISO 27001HIPAANIST CSFPCI DSSGDPRCIS ControlsOWASPMiCA / DORAMASVARAISO 27701+30 more

All Languages & Stacks

What Makes SWARM Different

Researcher-Level Depth, at Machine Speed

Four properties that separate SWARM from generic AI security scanners.

Multi-Agent Parallel Analysis

Five specialist LLMs run simultaneously in Phase A, each focused on a distinct domain: architecture, trust boundaries, data flow, state machines, and economic invariants.

Code-Anchored Findings

Every hypothesis must cite the exact file and line numbers that triggered it. No vague warnings, no generalized pattern alerts.

Semantic Deduplication

Cross-model findings are deduplicated by meaning, not wording. The same vulnerability identified from different angles gets merged, reducing noise by ~45%.

Validated Verdicts

Each finding is independently verified through execution path tracing. Every confirmation cites the specific code that proves the defect.

The SWARM Pipeline

Threat Model–Driven Multi-Phase Attack

Each phase builds directly on the last. No phase generates attack hypotheses without first establishing a structural understanding of the protocol.

A

Foundation Analysis

Structural understanding before any attack hypothesis.

Five specialist LLMs analyze the codebase in parallel, each from a different perspective. No attack hypotheses are generated here. This phase produces the foundational context that downstream phases build on: invariants, trust boundaries, and entry points.

B

Threat Hypothesis Generation

Code-anchored attack hypotheses at scale.

Six specialists generate concrete attack hypotheses informed by Phase A. Each specialist runs two passes with different LLMs to maximize coverage through model diversity. Every hypothesis must cite the exact file, line numbers, and the specific pattern that triggered it.

C

Semantic Deduplication

Signal without the noise.

Multiple specialists often identify the same vulnerability from different angles. Phase C consolidates semantic duplicates while preserving distinct findings, reducing the hypothesis set by roughly half before validation begins.

D

Validation

Every finding independently verified.

Each deduplicated hypothesis is validated through deep code analysis: verify the proof-of-signal exists in the actual code, trace the complete execution path from entry point to vulnerability, and confirm all preconditions are achievable.

E

Guided Agentic Deep Dive

Autonomous agents with full protocol context.

Autonomous agents (Claude Opus 4.6 and Codex 5.3) receive SWARM's full threat model as context: architecture, invariants, trust boundaries, confirmed findings, and refuted hypotheses from Phases A–D. They focus on integration boundaries, mathematical edge cases, and multi-step attack chains.

Phase A
Phase B
Phase C
Phase D
Phase E

“SWARM found critical vulnerabilities in our protocol that other well-known auditors had missed. The proof-of-concept exploits made it easy to understand the real impact and prioritize fixes.”

CTO|Top 5 TVL Project on Monad
Track Record

Proven Security Performance

SWARM is trained on a proprietary dataset of thousands of issues from hundreds of audits and real-world exploits.

100%
Outperformance Rate

SWARM consistently finds more critical vulnerabilities than traditional scanning tools

$2B+
TVL Secured

Trusted by protocols managing billions in total value locked

20K+
Vulnerabilities Identified

Real bugs found across hundreds of assessments

200+
Codebases Analyzed

Trained on a proprietary dataset of past audits and exploits

Who Is It For

Built for Teams That Ship Fast

Whether you're preparing for your first audit or managing ongoing security at scale, SWARM adapts to your workflow.

High-Iteration Teams

Teams shipping frequent updates who need continuous security feedback on every change. Get findings within minutes, not weeks.

  • Every code change evaluated
  • Real-time security feedback
  • Block vulnerable code before it ships

Pre-Audit Preparation

Resolve structural issues before human auditors begin. Ship cleaner code so auditors can focus on novel attack vectors.

  • Reduce audit scope and cost
  • Faster turnaround times
  • Less back-and-forth with auditors

Existing Security Teams

Complement your security team with automated, researcher-level analysis. SWARM handles systematic coverage so your team can focus on complex logic.

  • Force multiplier for auditors
  • Consistent coverage at scale
  • Surfaces leads for deeper review
SWARM vs Traditional Tools

A Fundamentally Different Approach

SWARM complements human auditors with capabilities that traditional tools simply cannot match.

Analysis Method
TraditionalSingle model or pattern matching
SWARMParallel multi-agent analysis across 5 specialist domains
Finding Quality
TraditionalGeneric vulnerability warnings
SWARMCode-anchored hypotheses with exact file and line references
Validation
TraditionalManual triage
SWARMAutomated proof-of-signal verification with full execution path tracing
Model Coverage
TraditionalSingle LLM provider
SWARMHeterogeneous ensemble across Claude, GPT, and Gemini
Language Support

Multi-Chain, Multi-Language Coverage

Solidity
Solidity
EVM smart contracts
Vyper
Vyper
Python-like EVM contracts
Rust
Rust
Solana, Near, Cosmos
MOV
Move
Aptos, Sui
CAI
Cairo
Starknet contracts
Ink!
Ink!
Substrate / Polkadot
Python
Python
Scripts, backends, agents
JavaScript
JavaScript
Node.js, dApps, tooling
TypeScript
TypeScript
Typed JS codebases
Go
Go
Infrastructure, relayers
Java
Java
Enterprise backends
C / C++
C / C++
Low-level systems
Swift
Swift
iOS, macOS
Kotlin
Kotlin
Android, JVM
FAQ

Frequently Asked Questions

What does SWARM stand for?

SWARM stands for Systemic Weakness Analysis & Remediation Mechanism. It's a multi-agent framework that operates across five phases: Foundation Analysis, Threat Hypothesis Generation, Semantic Deduplication, Validation, and Guided Agentic Deep Dive.

How does the five-phase pipeline work?

Phase A runs five specialist LLMs in parallel to establish structural understanding of the codebase. Phase B generates 50–80 code-anchored attack hypotheses using six specialists across two LLM passes each. Phase C deduplicates findings semantically, typically reducing the set by ~45%. Phase D validates each hypothesis through execution path tracing, assigning CONFIRMED, REFUTED, or CONTESTED verdicts. Phase E deploys autonomous agents with the full threat model as context to surface integration boundaries, mathematical edge cases, and multi-step attack chains.

What does 'code-anchored' mean for SWARM findings?

Every hypothesis generated in Phase B must cite the exact file, line numbers, and the specific code pattern that triggered it. This rules out vague or generalized alerts and ensures every finding can be immediately located and assessed by your team.

Does SWARM replace a traditional security audit?

No. SWARM resolves a large portion of structural and pattern-based issues before an audit begins, but human auditors still determine exploitability, assess economic risk, and evaluate system-level behavior. SWARM shrinks audit scope and reduces cost, but does not replace expert review.

What languages and chains are supported?

SWARM supports Solidity, Vyper, Rust (Solana, Near, Cosmos), Move (Aptos, Sui), Cairo (Starknet), and Ink! (Polkadot). Support expands continuously based on client needs.

Is SWARM safe to use with proprietary code?

Yes. SWARM runs on private, sandboxed infrastructure. Your code is never shared or used to improve models without explicit consent.

How fast does SWARM work?

Quick scans complete in under 10 minutes. Deep analysis takes 30–60 minutes. Full assessments complete in 2–4 hours depending on codebase size.