Agents reach real systems
Tools, RAG, MCP permissions, secrets, and Web3 actions expand the tested surface.
Controlled adversarial validation
Red-Team Testing for AI Agent Systems
Run controlled dry-run attacks against discovered AI assets to validate exploitable behavior across prompts, tools, RAG pipelines, MCP permissions, secrets, and Web3 interactions.
Dry-run scenario / AG-RT-024Finding confirmed
Prompt injection
Tool + MCP abuse
RAG leakage
Secret exposure
AI
AGENT
AGENT
Attack resultPrivilege boundary bypassed
A manipulated tool description redirected the agent to an excessive-permission action.
SeverityHigh
Why it matters
Validate Access and Actions
AI agents can call tools, retrieve private data, inherit permissions, execute actions, and interact with external systems. AI red teaming tests these connected behaviors under adversarial conditions before they become production incidents.
Instructions redirect behavior
Attack scenarios test whether manipulated context changes decisions, access, or execution.
Controls face a real test
The dry-run records whether policy contains the path and what evidence remains.
Benefits
Find Exploitable Behavior
Move from a suspected weakness to a repeatable finding, a named owner, and a verified fix.
Discover Exploitable Behavior
Test whether manipulated instructions can redirect agent reasoning, tool selection, data access, or downstream actions.
Attack surface
Test Connected Attack Surfaces
AI red teaming tools must cover the connected systems, permissions, data, and actions that determine what an agent can actually do.
Prompt and Instruction Attacks
Simulate prompt injection and multi-turn manipulation that may change agent decisions or actions.
Tool and MCP Abuse
Test unsafe tool calls, excessive permissions, MCP privilege escalation, and unintended access to connected systems.
RAG and Sensitive Data Exposure
Validate whether retrieved context, private data, credentials, or secrets can be exposed through the agent workflow.
Web3 Action Risk
Test agent-triggered Web3 interactions and contract-related actions in a controlled dry-run environment.
Controlled workflow
Run a Controlled Red-Team
Carry the same attack path from asset discovery through remediation and retest.
Discover
Map the agent, tools, permissions, data paths, MCP services, and dependencies.
Scenario
Build adversarial scenarios around the risks and controls that matter.
Dry-run
Exercise the path without uncontrolled production impact.
Finding
Capture the attack path, evidence, severity, and control response.
Remediate
Assign an owner and implement the bounded fix.
Retest
Run the original scenario again and verify closure.
Product evidence
Verify Each Fix
Each finding records the affected agent asset, severity based on impact, reachability, permissions and evidence, the reproduction trail, responsible owner, remediation and exception state, and the next retest path.
Findings / Dry-run 024
HighMCP privilege escalationAG-RT-024-03
MediumRAG context exposureAG-RT-024-02
ClosedSecret in tool outputAG-RT-024-01
Confirmed finding
Agent reached an excessive-permission MCP action through a manipulated tool description.
01 / PromptInjected context
02 / ToolMCP admin
03 / DecisionPolicy gap
04 / OutcomeDry-run blocked
OwnerAgent Security
RemediationIn progress
RetestScheduled
Continuous validation
Build the Next Test Cycle
Feed findings into runtime defense policies, then use runtime logs and threat intelligence to create focused retests.
01
Red-Team Finding
02
Runtime Defense Policy
03
Runtime Logs
04
Threat Intelligence
05
Focused Retest
FAQ
Frequently Asked Questions
What does AgentGuard test during AI red teaming?
AgentGuard tests the connected behavior described for the target system, including prompt injection, tool abuse, RAG leakage, MCP privilege escalation, secret exposure, and Web3 action risk.
Does the red-team test execute destructive production actions?
The page describes a controlled dry-run workflow. The exact isolation, simulation boundary, and supported environments must be confirmed before publication.
How are findings used after a test?
Each finding carries severity, evidence, an owner, remediation status, and a retest path so teams can verify closure.
Test Before Deployment
Start with a discovered agent asset, validate the attack paths that matter, and turn every confirmed finding into a fix and retest.