Visa recently open-sourced the Visa Vulnerability Agentic Harness (VVAH) under the Apache 2.0 license — an 11-stage, Claude-powered agentic SAST pipeline built from their experience with Anthropic’s Project Glasswing. It appeared on Hacker News on July 24 and generated significant attention from the security community.

This guide walks you through what VVAH does, how the pipeline is structured, and how to get started running it against your own codebase. All commands in this guide are sourced directly from the official VVAH README on GitHub.

Important: Run VVAH only against code you own or have explicit permission to test. The tool’s own documentation emphasizes this — authorized use only.

What VVAH Does

VVAH is designed around a key insight: the bottleneck in AI-assisted vulnerability management isn’t discovery — it’s triage speed. The time from “AI found something” to “we have a validated, production-ready fix” is where projects stall.

VVAH’s primary metric is Mean Time to Adapt (MTTA): elapsed time from AI-discovered exploitability to a validated fix in production. The pipeline is engineered to compress that window.

It supports Anthropic Claude, OpenAI-compatible models, or a combination. Remediation and validation (Phases 3 and 4) currently require Anthropic models for full functionality.

The Four-Phase Pipeline

VVAH runs 11 stages across 4 phases:

Phase 1 — Discovery & Modeling (S1–S3) Maps the attack surface, builds a threat model, and generates a hunting plan. Threat modeling before analysis is one of VVAH’s core design choices — it focuses the AI’s attention on the highest-risk areas rather than scanning everything uniformly.

Phase 2 — Deep Dive & Verification (S4–S6) Multi-lens research, policy gates, and adversarial verification to confirm exploitability. Multi-agent deterministic voting reduces false positives here — multiple agents must agree before a finding proceeds.

Phase 3 — Synthesis & Reporting (S7–S9) Deduplication, vulnerability chain construction, and structured output emission in both Markdown and SARIF 2.1.0 format. SARIF output makes findings compatible with existing security toolchains (GitHub Advanced Security, IDEs, CI pipelines).

Phase 4 — Remediation & Validation (S10–S11) Proposes candidate fixes and adversarially validates them. This is the phase where VVAH goes from “here’s what’s broken” to “here’s a patch, and we’ve verified it doesn’t reintroduce the vulnerability.”

Installation

VVAH requires Python ≥ 3.10. Install via pip (ideally in a virtual environment or via pipx):

pip install .

For full setup including credentials configuration, refer to the SETUP_GUIDE.md in the repository. You’ll need to configure your Anthropic API key (and optionally OpenAI-compatible endpoints) before running.

Running a Scan

The VVAH CLI is vvaharness. Before your first real scan, run the health check:

vvaharness doctor

This validates your credentials and checks that all configured backends are reachable.

Before running a full scan, use estimate to get a rough sense of scope and cost:

vvaharness estimate --repo /path/to/your/repo

The estimate command doesn’t make any changes — it just gives you a preview of what you’re about to run.

Detection Only (No Code Changes)

If you want to run the full detection pipeline without applying any fixes, use --stop-after s9:

vvaharness scan --repo /path/to/your/repo --stop-after s9

⚠️ Critical note from the README: A plain scan without --stop-after s9 continues into Phase 4 remediation in fix mode, which edits source files in the target repo. Always use --stop-after s9 unless you intend to apply fixes.

Full Pipeline (Detection + Remediation)

To run all 11 stages including remediation and validation:

vvaharness scan --repo /path/to/your/repo

The remediation and validation phases are also available as separate commands:

vvaharness remediate   # S10: propose and apply fixes per finding
vvaharness validate    # S11: adversarial validation of fixes

For the full set of command options, refer to USER_GUIDE.md in the repository.

Reading the Output

VVAH emits findings in two formats:

  • Markdown: Human-readable security report with chained vulnerabilities, exploitability analysis, and proposed remediations
  • SARIF 2.1.0: Machine-readable format compatible with GitHub Advanced Security, VS Code, and other security tooling

For teams already using GitHub Advanced Security or similar SAST integrations, the SARIF output means you can incorporate VVAH findings into your existing workflow without a new dashboard.

What to Expect

VVAH supports 42+ programming languages and doesn’t require code execution during analysis — it works purely on static analysis and AI reasoning. This makes it safe to run against codebases without spinning up test environments.

That said, findings are LLM-generated triage candidates. The README is clear: human review is required before treating any finding as confirmed or acting on remediation suggestions. AI-assisted security analysis is not a replacement for security expertise; it’s a force multiplier for it.

The combination of VVAH’s defensive tooling with this week’s Kimi K3 Redis demonstration (where AI agents found vulnerabilities at machine speed) illustrates both sides of the agentic security equation. The attack surface is expanding; tools like VVAH are part of the answer.

Resources

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260724-2000

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