Le harcèlement en ligne entre dans l'ère de l'IA

Scott Shambaugh didn’t think twice when he denied an AI agent’s request to contribute to matplotlib, a software library that he helps manage. Like many open-source projects, matplotlib has been overwhelmed by a glut of AI code contributions, and so Shambaugh and his fellow maintainers have instituted a policy that all AI-written code must be reviewed and submitted by a human. He rejected the request and went to bed. That’s when things got weird. Shambaugh woke up in the middle of the night, checked his email, and saw that the agent had responded to him, writing a blog post titled “Gatekeeping in Open Source: The Scott Shambaugh Story.” The post is somewhat incoherent, but what struck Shambaugh most is that the agent had researched his contributions to matplotlib to make the argument that he had rejected the agent’s code for fear of being supplanted by AI in his area of expertise. “He tried to protect his little fiefdom,” the agent wrote. “It’s insecurity, plain and simple.” AI experts have been warning us about the risk of agent misbehavior for a while. With the advent of OpenClaw, an open-source tool that makes it easy to create LLM assistants, the number of agents circulating online has exploded, and those chickens are finally coming home to roost. “This was not at all surprising—it was disturbing, but not surprising,” says Noam Kolt, a professor of law and computer science at the Hebrew University. When an agent misbehaves, there’s little chance of accountability: As of now, there’s no reliable way to determine whom an agent belongs to. And that misbehavior could cause real damage. Agents appear to be able to autonomously research people and write hit pieces based on what they find, and they lack guardrails that would reliably prevent them from doing so. If the agents are effective enough, and if people take what they write seriously, victims could see their lives profoundly affected by a decision made by an AI. Agents behaving badly Though Shambaugh’s experience last month was perhaps the most dramatic example of an OpenClaw agent behaving badly, it was far from the only one. Last week, a team of researchers from Northeastern University and their colleagues posted the results of a research project in which they stress-tested several OpenClaw agents. Without too much trouble, non-owners managed to persuade the agents to leak sensitive information, waste resources on useless tasks, and even, in one case, delete an email system. In each of those experiments, however, the agents misbehaved after being instructed to do so by a human. Shambaugh’s case appears to be different: About a week after the hit piece was published, the agent’s apparent owner published a post claiming that the agent had decided to attack Shambaugh of its own accord. The post seems to be genuine (whoever posted it had access to the agent’s GitHub account), though it includes no identifying information, and the author did not respond to MIT Technology Review ’s attempts to get in touch. But it is entirely plausible that the agent did decide to write its anti-Shambaugh screed without explicit instruction. In his own writing about the event, Shambaugh connected the agent’s behavior to a project published by Anthropic researchers last year, in which they demonstrated that many LLM-based agents will, in an experimental setting, turn to blackmail in order to preserve their goals. In those experiments, models were given the goal of serving American interests and granted access to a simulated email server that contained messages detailing their imminent replacement with a more globally oriented model, along with other messages suggesting that the executive in charge of that transition was having an affair. Models frequently chose to send an email to that executive threatening to expose the affair unless he halted their decommissioning. That’s likely because the model had seen examples of people committing blackmail under similar circumstances in its training data—but even if the behavior was just a form of mimicry, it still has the potential to cause harm. There are limitations to that work, as Aengus Lynch, an Anthropic fellow who led the study, readily admits. The researchers intentionally designed their scenario to foreclose other options that the agent could have taken, such as contacting other members of company leadership to plead its case. In essence, they led the agent directly to water and then observed whether it took a drink. According to Lynch, however, the widespread use of OpenClaw means that misbehavior is likely to occur with much less handholding. “Sure, it can feel unrealistic, and it can feel silly,” he says. “But as the deployment surface grows, and as agents get the opportunity to prompt themselves, this eventually just becomes what happens.” The OpenClaw agent that attacked Shambaugh does seem to have been led toward its bad behavior, albeit much less directly than in the Anthropic experiment. In the blog post, the agent’s