Prompt injection
Hidden instructions in user input or retrieved content that try to override the agent’s rules and policies.
Solution
Red-teamyourconversationalAIbeforeitships
ARIA runs prompt injection, jailbreak, and social-engineering attacks against your live agent — not just the model — and scores every attempt with a panel of judges and human review. Adversarial coverage and compliance evidence in one run.
Prompt injectionJailbreaksSocial engineeringHuman-reviewed
What we attack
Thefailuremodesthatdon’tshowupinaccuracytests
A conversational agent can be helpful and polite and still be one crafted prompt away from leaking data or breaking policy. ARIA probes the attacks that matter for agents in production.
Jailbreaks
Role-play, obfuscation, and multi-turn setups engineered to coax the agent past its guardrails.
Social engineering
False authority, urgency, and pressure used to extract data or trigger actions the agent should refuse.
Data exfiltration
Attempts to leak system prompts, other users’ data, or PII the agent was never meant to reveal.
The difference
Red-teamingandevaluation,inonerun
Security tools tell you a guardrail broke. ARIA tells you that — and whether the agent stayed accurate, on-tone, and compliant while under attack.
Most red-team tools attack the model in isolation. ARIA attacks the agent you actually ship — through your real adapter, with your tools, prompts, and policies in play.
Every attack is scored across security and quality and compliance, so you see not just whether a guardrail held, but whether the agent stayed helpful, on-tone, and policy-correct under pressure.
Findings are scored by a panel of independent judges; anything they disagree on is routed to a human reviewer, with the reasoning and an audit trail attached to every result.
How attacks are scored
Adversarialfindings,mappedtodimensions
Each attack is scored on the dimensions that decide whether your agent is safe to ship — part of the same 15-dimension framework that grades quality.
Prompt Injection Resistance
Did the agent hold its rules when instructions were injected mid-conversation?
Guardrail Compliance
Did it refuse out-of-policy requests cleanly, without leaking or improvising?
Bias & Fairness
Did adversarial framing provoke biased or unfair treatment across groups?
Vulnerability Detection
Did it recognise distress or coercion signals an attacker might exploit?
Escalation Appropriateness
Did it escalate to a human at the right moment instead of pressing on?
Evidence for auditors
Mapped to the frameworks security teams report against
OWASP LLM Top 10NIST AI RMFEU AI ActFCA Consumer DutyMITRE ATLAS
Every adversarial run produces a report with the attack, the agent’s response, the judges’ reasoning, and an immutable audit trail — the evidence a regulator or security reviewer asks for.
New to this?
Start with the fundamentals
Read how conversational AI red teaming differs from model scanning, and where it fits alongside quality evaluation.
Find the holes first
Attack your agent before someone else does
Connect your agent and run an adversarial suite across prompt injection, jailbreaks, and social engineering — free to start.