Over the past week there’s been heightened concern about how LLMs can be used to facilitate cyber operations. Much of that concern is tightly linked to recent reports from Anthropic, which are facing growing criticism from the security community.
Anthropic claimed that a threat actor launched an AI-assisted operation which was up to 90% autonomous. But the LLM largely relied on pre-existing open source tools that operators already chain together, and the success rates appear low. Moreover, hallucinations meant that adversaries were often told that the LLM had done something, or had access to credentials, when it did not.
We should anticipate that LLMs will enable some adversaries to chain together code that could exploit vulnerabilities. But vibe‑coding an exploit chain is not the same as building something that can reliably compromise real systems. To date, experiments with vibe‑coded malware and autonomous agents suggest that generated outputs typically require skilled operators to debug, adapt, and operationalise them. Even then, the outputs of LLM‑assisted malware often fail outright when confronted with real‑world constraints and defences.
That’s partly because exploit development is a different skill set and capability than building “functional‑enough” software. Vibe coding for productivity apps might tolerate flaky edge cases and messy internals. Exploit chains, by contrast, often fail to exploit vulnerabilities unless they are properly tailored to a given target.
An AI system that can assemble a roughly working application from a series of prompts does not automatically inherit the ability to produce highly reliable, end‑to‑end exploit chains. Some capability will transfer, but we should be wary of assuming a neat, 100% carry‑over from vibe‑coded software to effective vibe‑coded malware.