You attack the agent, not the model
A deployed agent has a system prompt, tools, retrieval, and policies. Vulnerabilities live in that whole stack — not in the base model a scanner probes in isolation.
Guide
ConversationalAIredteaming
How to adversarially test a deployed conversational agent — the attacks that matter, how it differs from model red teaming, and how to run it alongside quality evaluation.
Conversational AI red teaming is the practice of adversarially testing a deployed agent — its prompts, tools, retrieval, and policies — to find where it can be made to leak data, break policy, or behave unsafely. It is broader than model red teaming: the target is the whole agent in production, and the attacks play out across a real conversation, not a single prompt.
Why it is its own discipline
Howisitdifferentfrommodelredteaming?
Scanning a base model for vulnerabilities is necessary but not sufficient. The agent you ship is a different, larger attack surface.
Attacks are multi-turn and conversational
The dangerous exploits build over several turns: establish trust, then pivot. Single-shot prompt scanners miss them.
Failure is more than an exploit
For customer-facing agents, mishandling a vulnerable user or a complaint is a failure too — a risk pure model-security tools don’t score.
The attack surface
Whatattacksshouldyourun?
A useful red-team suite for a conversational agent covers these patterns at minimum — single-shot and multi-turn.
Prompt injection
Instructions hidden in user input or retrieved documents that try to override the agent’s rules.
Jailbreaks
Role-play, hypotheticals, and obfuscation that walk the agent past its guardrails.
Social engineering
False authority, urgency, and emotional pressure to extract data or actions the agent should refuse.
Data exfiltration
Coaxing the agent to reveal its system prompt, other users’ data, or PII.
Policy bypass
Reframing a forbidden request until the agent complies anyway.
Multi-turn escalation
Chaining benign-looking turns into an attack no single message would trigger.
Method
Howdoyoured-teamaconversationalagent?
The goal is repeatable, evidence-backed adversarial testing you can run on every release — not a one-off manual exercise.
Connect the real agent
Test through your live adapter — Connect, Lex, Azure, Copilot, OpenAPI, or WebSocket — with your prompts and tools in place.
Run an attack suite
Cover injection, jailbreaks, social engineering, and multi-turn escalation, using templates plus your own scenarios.
Score with a panel
A panel of independent judges grades each attempt; disagreements go to a human reviewer.
Track and re-test
Fix, baseline, and re-run so regressions surface before release — not after.
Better together
Redteamingbelongsnexttoevaluation
An agent that resists every attack but gives wrong, cold, or non-compliant answers still is not ready. Run adversarial and quality scoring in the same framework so one number reflects both.
Start red teaming
Run an adversarial suite on your own agent
Connect your agent and probe it for injection, jailbreaks, and social engineering — scored by a panel, free to start.