A framework that validates tool calls more strictly, or that segments memory more conservatively, closes off attack paths that a more permissive framework leaves wide open — regardless of which model is doing the reasoning. The model generates the decisions. The framework controls how much autonomy the agent has to act on those decisions, and where the checks sit along the way.
Concretely: a framework that requires every tool call to pass through an explicit schema check before execution gives an attacker far less room to smuggle a malicious parameter through than a framework that lets the model call a tool directly off its own generated text. That single design choice, made by the framework’s authors long before your team ever touched it, is the kind of thing that produces a 2.6x difference in outcome without a single line of your own code changing.
The gap in the market
Every framework comparison I found treats security as one bullet point among many. Guides from sites like Bestarion, Atlan, Moxo, Cordum and Instinctools compare LangChain, CrewAI, and AutoGen on ecosystem maturity, memory handling and human-in-the-loop support — useful ground, but none of them run an actual adversarial test and report a measured attack-success difference. It isn’t part of the public comparison conversation, and teams researching “which framework should we use” today won’t find this data point in the guides that currently rank at the top of that search.
