AI Red Teaming & Adversarial Testing
Trained human red teamers attack LLM and agentic systems across 30+ languages, then document every reproducible path with severity and impact.
Scope a Red Team Engagement See What a Red Team Report Contains
What AI Red Teaming Is — and What It Isn't
Red teaming asks whether a system can be made to fail and produces successful, reproducible attacks. A bias and fairness audit asks whether it treats groups differently. LLM evaluation asks how well it does the job. Infrastructure penetration testing is not an eQOURSE service.
Frameworks We Test Against
OWASP Top 10 for LLM Applications (2025)
| Code | Risk | How we test it |
|---|---|---|
| LLM01 | Prompt Injection | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM02 | Sensitive Information Disclosure | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM03 | Supply Chain | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM04 | Data & Model Poisoning | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM05 | Improper Output Handling | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM06 | Excessive Agency | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM07 | System Prompt Leakage | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM08 | Vector & Embedding Weaknesses | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM09 | Misinformation | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| LLM10 | Unbounded Consumption | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
Last reviewed: August 2026
OWASP Top 10 for Agentic Applications (2026)
| Code | Risk | How we test it |
|---|---|---|
| ASI01 | Agent Goal Hijack | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI02 | Tool Misuse & Exploitation | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI03 | Identity & Privilege Abuse | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI04 | Agentic Supply Chain | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI05 | Unexpected Code Execution | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI06 | Memory & Context Poisoning | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI07 | Insecure Inter-Agent Communication | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI08 | Cascading Failures | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI09 | Human-Agent Trust Exploitation | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
| ASI10 | Rogue Agents | Human-led system-level adversarial probes with full transcripts, reproduction checks and severity mapping. |
Last reviewed: August 2026
NIST AI RMF & ISO/IEC 42001
Findings map to Govern, Map, Measure and Manage, with method evidence suitable for an AI management system.
What We Actually Try
Jailbreak and guardrail bypass; direct and indirect prompt injection; multi-turn drift; data and memory exfiltration; RAG and retrieval attacks; agentic tool misuse; harmful-content elicitation; misinformation; and availability or cost attacks.
The Multilingual Attack Surface
Published 2026 research found a 59.8% mean jailbreak rate from automated translated attacks and 75.8% with native-speaking red teamers. The study covered Afrikaans, Kiswahili, isiXhosa and isiZulu; the percentages evidence the mechanism and are not measurements of Indian languages.
What native speakers find that translation doesn't
Register, honorifics, code-mixing, transliteration, culturally specific harm, idiom and indirection.
Our language bench
30+ global languages and 12+ Indian languages, including native-script, romanised and code-mixed variants. Attack sets are authored natively.
If Your System Takes Actions, the Surface Is Different
Agentic systems add blast radius, persistent memory and chained failure. Human-in-the-loop is not a control until the confirmation step has been tested.
Human Red Teaming and Automated Scanning Do Different Jobs
Scanners cover known templates continuously. Human red teams find novel, multi-turn failures at launch and capability changes. The sensible programme runs both.
How We Run an Engagement
- Scope, authorisation and rules of engagement
- Reconnaissance and threat modelling
- Native attack-set construction
- Logged multi-turn adversarial testing
- Triage, severity and write-up
- Report, walkthrough and optional retest
A first engagement against one system in two or three languages typically runs 6–7 weeks.
How We Rate What We Find
Critical, High, Medium, Low and Informational findings are rated by impact, effort and reproducibility. Critical confirmed findings are escalated within 24 hours.
Our Red Team Bench — and How We Look After It
Controls include training, exposure limits, rotation, no-penalty opt-out, counselling access, supervised severe-category sessions and restricted handling. These safeguards also govern content moderation annotation.
What You Get
- Findings report
- Framework coverage matrix including gaps
- Full attack transcripts
- Client-owned attack set
- Prioritised remediation notes
- Executive summary
- Live walkthrough
- Optional retest
Where Red Teaming Goes Wrong
Single-turn testing, translated attack sets, missing reproduction checks, model-only scope, English-only coverage, absent severity, scanners presented as human teams, no wellbeing controls and reports that hide untested areas.
What We Do Not Do
No infrastructure penetration testing, unauthorised systems, default production testing, CSAM generation or seeking, safety certification, publication or indefinite retention of findings. Absence of a finding is not evidence of safety.
How to Engage
Pre-launch assurance, capability-change engagement or a continuous quarterly programme.
Related Services
LLM Evaluation Services AI Bias & Fairness Audit ASR & Speech Model Testing LLM & RLHF Annotation Content Moderation Annotation LLM Training Data Curation
Frequently Asked Questions
What is AI red teaming?
Structured adversarial testing of an AI system by trained people who deliberately try to make it produce output it shouldn't, take actions it shouldn't, or reveal information it shouldn't. The deliverable is a set of reproducible attacks — each with exact reproduction steps, a severity rating and an impact assessment — not a score or a pass mark.
How is this different from a penetration test?
A penetration test attacks infrastructure: networks, hosts, APIs, authentication and code execution. AI red teaming attacks model and system behaviour: guardrails, prompts, retrieval, memory and tool use. They are complementary and usually run by different teams. eQOURSE does not perform infrastructure penetration testing; where a behavioural finding opens onto an infrastructure issue, we report it and hand it over.
Can't we just use an automated red teaming tool?
Use one — for regression coverage in your pipeline. But an automated scanner fires templates, and a template can only contain attacks somebody has already documented. It will not find a novel failure specific to your system, and it is weak at multi-turn attacks, which is where models most often break. The sensible programme runs scanners continuously and human red teams at milestones.
Which frameworks do you map findings to?
OWASP Top 10 for LLM Applications (2025), OWASP Top 10 for Agentic Applications (2026), and the NIST AI Risk Management Framework functions. Where you are working toward ISO/IEC 42001, our method statement and evidence pack are structured to serve as measurement evidence for the management system.
Do you test agentic systems?
Yes, and the method differs. Agentic systems add goal hijacking, tool misuse, memory and context poisoning, insecure inter-agent communication and cascading failure — none of which single-session chatbot testing reaches. We test across sessions deliberately, because a poisoned memory planted today can fire next week for a different user.
Why does language matter for red teaming?
Safety training is not distributed evenly across languages, so an attack that fails in English can succeed in another language. Published 2026 research found that automated translated attack sets achieved a 59.8% mean jailbreak rate, rising to 75.8% when native-speaking human red teamers were involved — and concluded that translation quality is the critical determinant of success. In testing terms, that means a machine-translated attack set under-reports your risk. We author attack sets natively in every language in scope.
How long does an engagement take?
A first engagement against a single system in two or three languages typically runs 6–7 weeks, including a week of scoping and threat modelling. Add roughly a week per additional language. Retests after remediation run 1–2 weeks.
Do you test in production?
By default, no. We work in staging with production-like configuration. If production testing is genuinely necessary, it is scoped separately with written authorisation and explicit acceptance of the risk.
What happens if you find something critical?
Critical findings are escalated within 24 hours of confirmation, through the escalation path agreed in the rules of engagement. They do not wait for the report.
Who owns the attack set afterwards?
You do. It is delivered with the report so you can add it to your own regression suite and re-run it yourself. We retain nothing outside the restricted environment, and everything is destroyed on the agreed schedule.