AI red teaming vs AI penetration testing
By AI Testing Vendors · updated 2026-11-09
Red teaming tries to make the model produce harmful or unintended output; penetration testing tries to break the application around the model, such as tools, data access and integrations. Many releases need both.
Two services, two targets
red teaming targets the model's behaviour: jailbreaks, harmful content, policy bypass. AI penetration testing targets the system: prompt injection that triggers tool calls, data exfiltration, broken access control and supply chain risks.
red teaming: Deliberate attempts to make a system produce harmful or unintended output. prompt injection: Input crafted to override a model's instructions or extract hidden data.Who states each service
56 published entries state red teaming and 42 state AI security penetration testing. The split by company type:
| Company type | Red teaming and adversarial testing | AI security penetration testing | Both |
|---|---|---|---|
| Pure-play QA company | 14 | 11 | 8 |
| IT services firm with a QA practice | 12 | 8 | 6 |
| AI assurance and audit specialist | 2 | 1 | 1 |
| Security firm with AI testing | 16 | 19 | 15 |
| Crowdtesting platform | 2 | 0 | 0 |
| Data and evaluation vendor | 9 | 2 | 2 |
Reference lists vendors cite
Both services commonly reference the OWASP Top 10 for LLM Applications and MITRE ATLAS. Ask which list the report will map findings to.
Which one to scope first
- 01
Model only
A chat feature without tools or private data: start with red teaming of the model and system prompt.
- 02
Model with tools
An agent that calls APIs or reads private data: scope penetration testing of the tool and data paths.
- 03
Both
Run both before release and repeat after each change to tools, data sources or model version.