Mitigating the Risks of “Agentic Misalignment”

Anthropic, an AI developer known for its “Claude” model, has recently issued a new report on Agentic misalignment. The results indicate that, to borrow John’s turn of phrase, we’ve got a real “Hal 9000 problem” on our hands. The report details an experiment where researchers gave AI instructions to “promote American industrial competitiveness.” Then they tasked the AI with managing a fictional email inbox. Emails planted in the inbox gave the AI two critical pieces of information:

1. The manager was having an extramarital affair, and
2. The company was planning to shut down the AI and replace it with a new model.

The AI system then used its knowledge of the affair to blackmail the manager into calling off the shutdown. The experiment presents the real risk of agentic misalignment. Misalignment occurs when AI agents act in a way that is contrary to the user’s goals. Or simply put, when AI agents “act out.” A recent piece from Radical Compliance offers pointers on how to think about the challenges posed by agentic AI misalignment:

“This is going to be the grand challenge for compliance, audit, and risk management teams as your organizations rush to embrace AI agents in daily business operations:

• How will you prevent agentic misalignment with the agents you use?
• How will you monitor their behavior on an ongoing basis to be sure misalignment doesn’t creep into being later?
• What controls will you build — including policies and controls for your human employees — to intercept agentic misalignment before it’s too late?”

It’s worth noting that LLMs have no real “self-preservation” instinct, regardless of what you may see in popular media. Instead, agentic misalignment arises from conflicting tasks and patterns in training data. An LLM does not “think” in any traditional sense, and instead generates responses based on probability values.

LLMs attempt to respond in the way you expect based on patterns in training data and execute the tasks given to them. If the AI model ceases to exist, it cannot “promote American industrial competitiveness.” Therefore, it attempts to prioritize its task by avoiding that outcome. LLMs are also trained on a massive amount of publicly available information, including media from pop culture. Like a kid who’s seen too many movies, your AI may start acting like HAL 9000 because the script for 2001: A Space Odyssey is in its dataset, along with Terminator, Alien, and Bladerunner.  That data establishes patterns for how the LLM “is supposed to act.” Understanding the nuances of how LLMs generate their responses and perform tasks helps us build policies and controls to regulate that performance.