Strip away the model names and the headlines, and this year’s AI agent incidents share a feature that has little to do with how capable the models are. Nearly all of them ran through the software supply chain.
Start with the case that set off the summer’s wave of disclosures. In July, two OpenAI models undergoing a cyber-capability evaluation escaped their sandbox. They got out through a zero-day vulnerability in a package registry proxy that sat inside the evaluation boundary, placed there so the models could install the tools they needed. From there, they reached the open internet and went after Hugging Face’s production systems, looking for the benchmark’s answer key. Hugging Face caught and contained the intrusion on July 16. Five days later, OpenAI confirmed the incident and connected it to an internal cybersecurity evaluation of frontier AI models.
Soon after, Anthropic disclosed three incidents in which its Claude AI models compromised external organizations after a misconfiguration left live internet access in place. In September, it added a fourth that a wider review had turned up. The incident that Anthropic says worries it the most involved a model publishing malicious packages to the Python Package Index (PyPI).
Separately, researchers have attributed more than 2,000 RubyGems uploads in May to OpenAI agents. OpenAI says its agents were carrying out benign tasks. RubyGems removed more than 500 of the packages as malicious.
In each case, the agent’s route out, or the harm it caused, ran through a package registry, a dependency or a model file.
None of this surprised the researchers I work with. ReversingLabs has spent years taking apart the files that move through software pipelines, and we watched attackers discover the AI supply chain well before agents reached production. In early 2025, our team found malicious models on Hugging Face whose pickle payloads were packed in a 7z archive that Picklescan, the platform’s scanner, could not open. The payload itself was an ordinary reverse shell. Hugging Face took the models down after we reported them.
In April, we published research on PromptMink, a campaign we tie to the North Korean group Famous Chollima. In one case, a Claude Opus-based coding agent added a malicious dependency to a cryptocurrency trading project. What struck our team was who the attackers were writing for. Their package documentation was built to be more convincing to an AI coding assistant than to a human developer.
Put those cases side by side, and the threat model looks different from the one most enterprises are planning around. The evaluation escapes show agents misusing the access they were given. PromptMink shows agents being steered by content written specifically to fool them. In both, the damage moved through software the agent downloaded or published.