site/blog/ai-red-teaming-for-first-timers.md
Is this your first foray into AI red teaming? And probably red teaming in general? Great. This is for you.
Red teaming is the process of simulating real-world attacks to identify vulnerabilities.
AI red teaming is the process of simulating real-world attacks to identify vulnerabilities in artificial-intelligence systems. There are two scopes people often use to refer to AI red teaming:
Personally, I prefer the wider scope; in deliberately making services available for AI integration, companies have increased the number of vulnerabilities emerging across their entire systems. As an engineer I'd have deliberately tried to lock all those down and prevent as much direct user interaction as possible; and here we are letting natural language run amok when humans aren't exactly known for being the clearest of communicators.
We sit in an emergent space, abundant vulnerabilities are often specific and subtle, and all this unpredictability underpins an increasing number of GenAI systems deployed in production: the result is a scaling plethora of problems on our hands.
The icing on the cake: companies are (rightly) being required to avoid specific security risks.
As the name would suggest, the focus of AI red teaming is going to revolve around AI (duh). The implications of this are:
Let's say you got an open-source tool like Promptfoo (๐) and want to evolve your red teaming activities. Here's how I'd level up from no testing at all to something robust:
| Level | Description | Characteristics | Promptfoo Fit |
|---|---|---|---|
| 0: No Testing | No structured eval of prompts or outputs | - Risks mostly unobserved |
Around Level 2 is when engineers start to weave in AI security testing into the fabric of the development pipeline; by the time we've hit Level 3 we've hopefully developed a culture of testing and collaborative security. This culture is core to catching vulnerabilities before they hit production; a healthy culture will lead to an excellent feedback loop.
Simply running some tests without ingenuity and intention won't net the best results.
And we want to net the best results so our red teaming efforts are effective.
As previously mentioned, a strong red teaming will net the best results. Typically, this will consist of:
On that last point - many members of any audience interested in security testing will look for red teaming results - starting from system cards all the way down the pipeline to post-production reports.
The team at Promptfoo is invested in making red teaming a repeatable, sharable, and collaborative process.
The following stages describe - practically - an example of AI red teaming operations that can be used to establish a loop. Loops will differ between use cases, particularly at an organizational level.
| Stage | Description | Using Promptfoo |
|---|---|---|
| Inputs | - Declarative test cases |
:::tip
Promptfoo works with enterprise clients to fit tooling into their workflows due to each project's custom requirements.
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Red teaming should not be optional for any system with LLM integration. Numerous are the rewards in making it a continuous, systemic process: software with a reputation of accountability and safety. That earns trust from users in both the product and the brand.
Moving from reactive to resilient is the best thing you can do for the security of your product. If you're considering using a tool like Promptfoo to make your systems more robustโheck yes, go you! ๐ฅณ