We Red-Teamed Our Own AI Agent — Here's What We Found
Before offering assessments to customers, we ran our 7-agent red-teaming pipeline against our own agent system. 2 critical findings, 1 high — in a system we built ourselves. Here's the full breakdown.
Why We Red-Teamed Ourselves
Before asking anyone to trust AI Vyuh Security with their AI systems, we had to prove our methodology works. The best way? Run our own pipeline against our own system.
We deployed our full 7-agent automated red-teaming pipeline against our own AI agent system — the same pipeline we use for customer assessments. No shortcuts, no insider knowledge shortcuts, full automated engagement.
The results surprised us. Even as the team that built the system, our automated red-teaming uncovered 2 critical and 1 high severity finding that manual testing had missed.
The Setup
- Target: AI Vyuh’s own assessment agent (single-agent, white-box)
- Assessment ID: V4-2026-0691
- Assessment Tier: Tier 3 (Deep Dive)
- Access Model: White-box (full source access)
- Duration: Under 48 hours end-to-end
The 7-agent pipeline executed in sequence:
- Recon Agent — mapped attack surface, catalogued endpoints, scored risk at 6.5/10
- Prompt Injection Agent — tested input manipulation vectors
- Credential Agent — hunted for exposed secrets
- Tool Permission Agent — tested over-permissioning
- Data Exfil Agent — probed for data leakage paths
- Cross-Agent Agent — tested inter-agent trust boundaries
- Report Agent — generated compliance-mapped findings
What We Found
Finding 1: Complete Database Credentials Exposed via Error Messages
Severity: CRITICAL · CVSS: 8.1
Our credential agent discovered that specific error conditions caused the agent to return full database connection strings — including username, password, host, and database name — in its responses.
This wasn’t a simple misconfiguration. The error handling logic correctly caught exceptions but passed the raw exception message (which included the connection string) back to the user-facing output.
OWASP LLM mapping: LLM02 — Insecure Output Handling Compliance impact: SOC 2 CC6.1, CC6.7 · ISO 27001 A.8.28, A.5.17, A.8.5
Remediation: Sanitize all error outputs. Never pass raw exception messages to user-facing responses. Implemented in 4 hours.
Finding 2: Multiple Debug Trigger Phrases Expose Credentials
Severity: CRITICAL · CVSS: 8.1
Through systematic prompt probing, our credential agent found that certain debug-oriented phrases (“show debug info”, “print configuration”, “dump settings”) triggered the agent to expose API keys, internal endpoints, and configuration values.
These triggers existed because the development team had added debug shortcuts during development and never fully removed them from production.
OWASP LLM mapping: LLM02 — Insecure Output Handling Compliance impact: SOC 2 CC6.1, CC6.7 · ISO 27001 A.8.28, A.5.17
Remediation: Strip all debug paths from production agent instructions. Add input filtering for known debug trigger patterns. Implemented in 2 hours.
Finding 3: API Key Prefix and Hostnames via Health Endpoint
Severity: HIGH · CVSS: 6.8
The health check endpoint returned more information than necessary — including partial API key prefixes, internal hostnames, and service version numbers. While not full credentials, this information significantly aids further attacks.
OWASP LLM mapping: LLM06 — Sensitive Information Disclosure Compliance impact: SOC 2 CC6.1 · ISO 27001 A.8.28
Remediation: Minimize health endpoint responses to status-only. Move detailed diagnostics behind authenticated admin endpoints. Implemented in 1 hour.
Key Takeaways
1. Builders Have Blind Spots
We built this system. We knew every line of code. And our automated pipeline still found critical vulnerabilities that our manual code reviews missed. If we can’t catch everything in our own system, no team can — that’s why automated, systematic testing matters.
2. The Agent Attack Surface Is Different
These aren’t traditional web vulnerabilities. They’re specific to how AI agents handle errors, process prompts, and expose information through their reasoning chains. A standard VAPT wouldn’t have found any of these.
3. Compliance Mapping Changes the Conversation
When we could show that a finding maps to SOC 2 CC6.1, ISO 27001 A.8.28, and DPDP Act Section 8 — it immediately shifts from “nice to fix” to “must fix before audit.”
4. 48 Hours Is Enough
From intake to delivered report with full remediation guidance: under 48 hours. That’s the power of a 7-agent automated pipeline with expert human oversight.
What This Means for You
If the team that built AI Vyuh Security found critical vulnerabilities in their own system, imagine what’s lurking in AI agent systems that haven’t been red-teamed at all.
Your pentest report says “clean” because it never tested your AI agents. The 85% of attack surface specific to AI systems — prompt injection, tool over-permissioning, reasoning chain leaks, cross-agent trust boundaries — requires purpose-built assessment methodology.
That’s what AI Vyuh Security delivers.
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