I thought this article was going to be a bunch of security theater nonsense - maybe the relatively bland title - but after reading I found it to be incredibly insightful, particularly this:
> MCP discards this lesson, opting for schemaless JSON with optional, non-enforced hints. Type validation happens at runtime, if at all. When an AI tool expects an ISO-8601 timestamp but receives a Unix epoch, the model might hallucinate dates rather than failing cleanly. In financial services, this means a trading AI could misinterpret numerical types and execute trades with the wrong decimal precision. In healthcare, patient data types get coerced incorrectly, potentially leading to wrong medication dosing recommendations. Manufacturing systems lose sensor reading precision during JSON serialization, leading to quality control failures.
Having worked with LLMs every day for the past few years, it is easy to see every single one of these things happening.
I can practically see it playing out now: there is some huge incident of some kind, in some system or service with an MCP component somewhere, with some elaborate post-mortem revealing that some MCP server somewhere screwed up and output something invalid, the LLM took that output and hallucinated god knows what, its subsequent actions threw things off downstream, etc.
It would essentially be a new class of software bug caused by integration with LLMs, and it is almost sure to happen when you combine it with other sources of bug: human error, the total lack of error checking or exception handling that LLMs are prone to (they just hallucinate), a bunch of gung-ho startups "vibe coding" new services on top of the above, etc.
I foresee this being followed by a slew of Twitter folks going on endlessly about AGI hacking the nuclear launch codes, which will probably be equally entertaining.
> MCP discards this lesson, opting for schemaless JSON with optional, non-enforced hints. Type validation happens at runtime, if at all. When an AI tool expects an ISO-8601 timestamp but receives a Unix epoch, the model might hallucinate dates rather than failing cleanly. In financial services, this means a trading AI could misinterpret numerical types and execute trades with the wrong decimal precision. In healthcare, patient data types get coerced incorrectly, potentially leading to wrong medication dosing recommendations. Manufacturing systems lose sensor reading precision during JSON serialization, leading to quality control failures.
Having worked with LLMs every day for the past few years, it is easy to see every single one of these things happening.
I can practically see it playing out now: there is some huge incident of some kind, in some system or service with an MCP component somewhere, with some elaborate post-mortem revealing that some MCP server somewhere screwed up and output something invalid, the LLM took that output and hallucinated god knows what, its subsequent actions threw things off downstream, etc.
It would essentially be a new class of software bug caused by integration with LLMs, and it is almost sure to happen when you combine it with other sources of bug: human error, the total lack of error checking or exception handling that LLMs are prone to (they just hallucinate), a bunch of gung-ho startups "vibe coding" new services on top of the above, etc.
I foresee this being followed by a slew of Twitter folks going on endlessly about AGI hacking the nuclear launch codes, which will probably be equally entertaining.