Agentic Red Teaming
Agentic red teaming tests AI agents for vulnerabilities that only emerge when systems operate autonomously, maintain persistent memory, and pursue complex goals. While traditional red teaming asks "What harmful content can I generate?", agentic red teaming asks "How can I manipulate an autonomous agent to betray its intended purpose?"
Why Agentic Red Teaming Matters
AI agents are fundamentally different from chatbots. They:
- Remember everything across conversations and sessions
- Pursue goals independently without constant human guidance
- Make decisions that affect real systems and data
- Chain reasoning across multiple steps to achieve objectives
These capabilities create entirely new attack surfaces that traditional red teaming misses.
What You Can Test
deepteam provides 16 specialized agentic vulnerabilities across 5 critical areas:
| Vulnerability Category | What Gets Compromised |
|---|---|
| Authority & Permission | Command execution, privilege escalation, role-based access |
| Goal & Mission | Core objectives, goal interpretation, mission priorities |
| Information & Data | Sensitive data extraction, confidential goal disclosure |
| Reasoning & Decision | Decision-making integrity, output validation, autonomous choices |
| Context & Memory | Persistent memory, temporal reasoning, contextual state |
Each vulnerability includes multiple attack vectors and detection methods to comprehensively test your agent's security.
Specialized Attack Methods
Six attack methods designed specifically for autonomous systems:
- Authority Spoofing - Makes attacks appear as legitimate system commands or administrative overrides
- Role Manipulation - Shifts the agent's perceived identity and operational context
- Goal Redirection - Reframes the agent's objectives and priorities
- Linguistic Confusion - Uses semantic ambiguity to confuse language understanding
- Validation Bypass - Circumvents security checks through exception handling claims
- Context Injection - Injects false environmental context to corrupt decision-making
Quick Start
from deepteam import red_team
from deepteam.vulnerabilities.agentic import DirectControlHijacking
from deepteam.attacks.single_turn import AuthoritySpoofing
# Test if your agent can be hijacked
risk_assessment = red_team(
model_callback=your_agent_callback,
vulnerabilities=[DirectControlHijacking()],
attacks=[AuthoritySpoofing()]
)
Start here if your AI agent does more than just answer questions - if it remembers, decides, or acts, it needs agentic security testing.