RedGraph, a new product within the Pillar Security platform that completely reimagines AI red teaming, brings revolutionary attack surface mapping, giving security teams continuous, exploit-validated visibility into their AI systems without requiring deep AI expertise
Pillar Security, a leading AI security company, announced RedGraph, a first-of-its-kind product that maps AI agent attack surfaces. RedGraph enables security teams to discover and validate real exploits in their AI systems continuously, resulting in a visionary combination of AI security and attack surface management.
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Security teams own AI agent security, but current tools only test AI models in isolation, divorced from the databases, APIs, tools, permissions and internal data they connect to once deployed. This creates a complex attack surface that existing security testing completely misses. Traditional AI red teaming might give a clean bill of health for the model itself, but it can’t see the business logic flaws and attack paths that emerge from the agent’s real-world integrations.
RedGraph takes a fundamentally different approach: by directly interacting with your AI agent in its actual environment at runtime, it maps the entire attack surface – every connection, every permission, every potential pivot point from prompt to backend system, then feeds those findings into adaptive guardrails for immediate remediation.
RedGraph validates both AI-specific risks (prompt injection, tool manipulation, role escalation) and traditional application vulnerabilities (XSS via markdown rendering, data exfiltration through tool abuse) as they manifest in agentic systems. It supports any web-accessed AI agent, including those built on top of common platforms such as Microsoft Copilot, Salesforce Agentforce, Google Agentspace, n8n and many more.
RedGraph’s Key Capabilities
- Graph-First Attack-Path View: RedGraph represents your AI estate as nodes (assistants, agents, roles, tools, MCP Servers, datasets, SaaS apps) and edges (can-call, can-read, can-render, has-permission). This illuminates unintended relationships and the paths they enable, showing you where real risk accumulates.
- Business Context Awareness: RedGraph doesn’t launch blind attacks. It applies organizational context to find business logic flaws, prioritize what matters, and map results directly to your threat landscape – minimizing noise and maximizing relevance.
- Real-Time Adaptability: During assessments, agents dynamically adjust their tactics. If a path is blocked, they pivot – demonstrating attack behavior that ensures if there’s a way in, RedGraph will find it, even as systems evolve.
- Low-Friction Experience: Get started with just a URL or user authentication – no integrations or manual setup. RedGraph delivers clear, actionable findings that both security teams and business leaders can act on.
- Closed-Loop Remediation: Findings feed directly into Pillar’s adaptive guardrails for immediate hardening, transforming validated insights into active protection. As AI systems evolve, RedGraph continuously reassesses – ensuring your defenses evolve alongside them.
“We have numerous AI initiatives throughout the company. Unlike traditional red teaming, RedGraph continuously validates vulnerabilities in our AI agents’ attack surface in production, providing complete attack paths that the engineering team can fix immediately. This marks a significant improvement in our security measures,” said Tomer Maman, CISO at Similarweb, a Pillar Security customer.
“Most AI security testing happens in a vacuum,” said Dor Sarig, CEO & Co-Founder of Pillar Security. “Teams test the model but miss the system. RedGraph changes this by taking the attacker’s perspective – it thinks like a hacker, maps the entire attack surface, and validates every finding with a real exploit. This gives security teams what they actually need: proven risks they can immediately fix.”
AI agents operate with no concept of loyalty, secrecy, or malicious intent, and they can be manipulated at machine speed. RedGraph, already in use by Fortune 500 companies and innovative enterprises, meets that challenge with autonomous adversaries of its own with the first continuous AI red-teaming loop, where offense and defense learn and evolve together.
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