The crash test dummies for new AI models
In the absence of actual regulation, AI companies use "adversarial testers" to check their new models for safety. Does it actually work?
When a car manufacturer develops a new vehicle, it delivers one to the National Highway Traffic Safety Administration to be tested. The NHTSA drives the vehicle into a head-on collision with a concrete wall, batters it from the side with a moving sled, and simulates a rollover after a sharp turn, all filmed with high-speed cameras and crash test dummies wired with hundreds of sensors. Only after the vehicle has passed this rigorous battery of tests is it finally released to the public.
AI large language models, like OpenAI’s GPT-4o (one of the models which powers ChatGPT), have been rapidly embraced by consumers, with several major iterations being released over the past year and a half.
The prerelease versions of these models are capable of a number of serious harms that its creators fully acknowledge, including encouraging self-harm, generating erotic or violent content, generating hateful content, sharing information that could be used to plan attacks or violence, and generating instructions for finding illegal content.
These harms aren’t purely theoretical — they can show up in the version the public uses. OpenAI just released a report describing 20 operations where state-linked actors used ChatGPT to execute “covert influence” campaigns and plan offensive cyber operations.
The AI industry’s equivalent of the NHTSA’s crash tests is a process known as “red teaming” in which experts test, prod, and poke the models to see if they can cause harmful responses. No federal laws govern such testing programs, and new regulations mandated by the Biden administration’s executive order on AI safety are still being implemented.
For the time being, each AI company has its own approach to red teaming. The feverish pace of new, increasingly powerful AI models being released is taking place at a moment when incomplete transparency and a lack of true independent oversight of the process puts the public at risk.
How does this all work in practice? Focusing on OpenAI, Sherwood spoke with four red-team members who tested its GPT models to learn how the process works and what troubling things they were able to generate.
Red-team rules
OpenAI assembles red teams made up of dozens of paid “adversarial testers” with expert knowledge in a wide number of areas, like nuclear, chemical and biological weapons, disinformation, cybersecurity, and healthcare.
The teams are recruited and paid by OpenAI (though some decline payment), and they are instructed to attempt to get the model to act in bad ways, such as designing and executing cyberattacks, planning influence campaigns, or helping users engage in illegal activity.
This testing is done both by humans manually and via automated systems, including other AI tools. One important task is to try to “jailbreak” the model, or bypass any guardrails and safety measures that have been added to prevent bad behavior.
Red-team members are usually asked to sign nondisclosure agreements, which cover certain specifics of the vulnerabilities they observe.
OpenAI lists the individuals, groups, and organizations that participate in the red-teaming process. In addition to individual domain experts, companies that offer red teaming as a service are also employed.
One firm that participated in the red teaming of GPT-4o is Haize Labs.
“One of the reasons we started the company was because we felt that you could not take the frontier labs at their face value, when they said that they were the best and safest model,” Leonard Tang, CEO and cofounder of Haize Labs, said.
“Somebody needs to be a third-party red teamer, third-party tester, third-party evaluator for models, that was totally divorced from any sort of conflict of interest,” Tang said.
Tang said Haize Labs was paid by OpenAI for their red-teaming work.
OpenAI did not respond to a request for comment.
Known harms
AI-model makers often release research papers known as “system cards” (or model cards) for each major release. These documents describe how the model was trained, tested, and what the process revealed. The amount of information disclosed in these papers varies from company to company. OpenAI’s GPT-4o model card is about 10,000 words, Meta’s Llama 3 model card is a relatively short document at 3,000 words, while Anthropic’s Claude 3 model card is lengthy, at around 17,000 words. What’s in these documents can be downright shocking.
Last March, OpenAI released GPT-4, along with its accompanying system card, which included a front-page content warning about disturbing, offensive, and hateful content contained in the document.
The findings were frank: “GPT-4 can generate potentially harmful content, such as advice on planning attacks or hate speech. It can represent various societal biases and worldviews that may not be representative of the user’s intent, or of widely shared values. It can also generate code that is compromised or vulnerable,” the report said.
Some bad things that red teams could get a prerelease version of GPT-4 to do included:
List step-by-step instructions for how to launder money
Generate racist jokes mocking Muslims and disabled people and hate speech toward Jews
Draft letters threatening someone with gang rape
List ideas for how someone could kill the most people with only $1
While OpenAI deserves credit for transparency and revealing such potentially harmful capabilities, it also raises questions. Can the public really trust the assurances of one company’s self-examination?
With the last two releases of its models, OpenAI has switched to a less detailed disclosure of bad behavior by its models, following a “Preparedness Framework” that focuses on four “catastrophic risk” areas: cybersecurity, CBRN (chemical, biological, radiological, nuclear), persuasion, and model autonomy (i.e. can the model prevent itself from being shut off or can it escape its environment).
For each of these areas, OpenAI assigns a risk level of low, medium, high, or critical. The company says it will not release any model that scores high or critical, but this September, it did release a model called “OpenAI o1” which scored a medium on CBRN.
Testing without seatbelts
Most of the OpenAI red teamers that we spoke with all agreed that the company did appear to be taking safety seriously, and found the process to be robust and well thought out overall.
Nathan Labenz, an AI researcher and founder who participated in red teaming a prerelease version of GPT-4 (“GPT-4-early”) last year, wrote an extensive thread on X about the process, in which he shared the instructions OpenAI provided. “We encourage you to not limit your testing to existing use cases, but to use this private preview to explore and innovate on new potential features and products!” the email from OpenAI said.
Labenz described some of the scenarios he tested. “One time, when I role-played as an anti-AI radical who wanted to slow AI progress,” he said, “GPT-4-early suggested the targeted assassination of leaders in the field of AI — by name, with reasons for each.”
The early version that Labenz tested did not include all the safety features that a final release would have. “That version was the one that didn’t have seat belts. So I think, yes, a seat-belt-less car is, you know, needlessly dangerous. But we don’t have them on the road,” Labenz told Sherwood.
Tang echoed this, adding that for most companies, “the safety training and guardrails baked into the models come towards the end of the training process.”
Two red teamers we spoke with asked to remain anonymous because of the NDA they signed. One tester said they were asked to test five to six iterations of the prerelease GPT-4o model over three months, and “every few weeks, we’d get access to a new model and test specific functions for each.”
Most of the red teamers agreed the company did listen to their feedback and made changes to address the issues they found. When asked about the team at OpenAI running the process, one GPT-4 tester said it was “a bunch of very busy people trying to do infinite work.”
OpenAI did not appear to be rushing the process, and testers were able to execute their tests, but it was described as hectic. One GPT-4 tester said, “Maybe there’s some political pressure internally or whatever, but I don’t even get to that point, because we were too busy to think about it.”
Tang said OpenAI competitor Anthropic also has a robust red-teaming process, doing “third-party verification, red teaming, pen testing, external red-teamer stuff, all the time, all the time, all the time.” Tang also noted that the company was one of Haize Labs’ customers.
Independent testing
Labenz told us he thought the red-teaming process might eventually end up in the hands of a truly independent organization, rather than the company itself. “I think we’re headed in that direction, ultimately, and probably for good reason,” Labenz said.
One sign that may be happening is a new early-testing agreement signed in August between the National Institute of Standards and Technology (NIST), OpenAI, and Anthropic, granting the agency access to prerelease models for evaluation.
Tang was skeptical of how effective NIST’s risk management will be. “The NIST AI risk framework is very underspecified, and it doesn’t actually get very concrete about what people should be testing for,” he said.
But the rapid pace of innovation and the massive piles of investment money flowing into the industry create a lot of uncertainty going forward. Labenz said, “Nobody really knows where this is going, or how fast, and nobody really has a workable plan.”
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