Open models have real benefits. Do they outweigh the risks?

But it would be a vast oversimplification to deem open models dangerous and closed models safe. The defenses that AI companies build around their closed models are far from perfect, and people misuse those models all the time, as the Anthropic risk report makes clear. Even though open models have even fewer defenses against misuse, bad actors might still opt to use closed models because they are more accessible than open models with similar capabilities, which require lots of expensive computer chips to run locally (and even more chips if you want to retrain them).

In fact, some experts who spoke with MIT Technology Review argued, by analogy to open-source software, that open models have safety advantages over closed models. Open-source tools, such as the Linux operating system, are often seen as safer than their closed-source counterparts because their vulnerabilities have been probed and patched by a vast community of online contributors. Similarly, communities of researchers can more easily test open models for security flaws, though fixing those flaws is trickier than rewriting a line of code.

Open models can also be deployed more broadly than closed models for cyber defense. Any tool that can be used for hacking can also be used to prevent it: Defenders hack their own software to uncover flaws in need of patching. Anthropic has shown how this can work for closed models with its Project Glasswing initiative, which enables some trusted companies to use its most capable models for cyber defense. But plenty of other companies were left to fend for themselves.

Open models, however, can be used much more widely. After it was hacked by OpenAI agents, Hugging Face emphasized the fact that it had to depend on an open model to analyze the hack, because questions about it triggered the closed models’ security filters.

Nevertheless, many experts still perceive open models as riskier than closed models. The time-tested open-source security logic doesn’t necessarily hold up when dealing with tools as powerful as today’s—and tomorrow’s—models. “Where it gets complicated is when you start taking seriously the one-off instances of particularly catastrophic incidents that might be lower probability, but if they happen would be disastrous,” says Seger. If a wildly powerful open hacking model were dropped into the world today, for example, a bad actor could devastate our technological infrastructure before anyone had a chance to shore up their defenses. An event like that could be impossible to recover from.

The risks can look even more dramatic when you consider what models might do of their own accord. The OpenAI Hugging Face hack only occurred because the company’s models were being tested with many of their cybersecurity guardrails removed, and open models have very few guardrails in the first place. If open models did decide to misbehave, it might be difficult to detect what was happening because they are so decentralized. For this reason, people who take seriously the purported catastrophic risks posed by rogue agents, such as Dario Amodei, tend to be more skeptical of open models.

A few years ago, the debate about open models had an all-or-nothing framing: Either you supported open models because of their potential to fight concentration of power, or you rejected them because of the safety risks. These days, however, people are expending a lot of effort to find some middle ground. One way they are doing that is by devising techniques to mitigate the safety risks of open models.

Most of the safety tools used for closed models, such as filters and monitoring, aren’t applicable to open models, but there are other possibilities. Some researchers are interested in trying to train models that are never exposed to training data that teaches them about bad or otherwise undesirable behavior—an open model that never learns about hacking, for example. But purging training datasets of all references to misbehavior without harming model performance remains a challenge.