From 6412c2c0465539881af058e0c438641d96e2ea38 Mon Sep 17 00:00:00 2001 From: James Yang Date: Thu, 27 Aug 2026 15:56:30 -0400 Subject: [PATCH] Define the name length mitigation in ASCII code points. The mitigation section said "characters", which is ambiguous. The name rule already uses ASCII code points and a 128 limit. --- index.bs | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/index.bs b/index.bs index 241bb2c..fc9e901 100644 --- a/index.bs +++ b/index.bs @@ -1775,11 +1775,11 @@ respective private browsing modes are safely exposed to [=agents=] and that thes

Restricting maximum input lengths

-**What:** Restrict the maximum amount of characters +**What:** Restrict the maximum number of code points **Threats addressed:** [[#metadata-description-attacks]] -**How:** This restriction would not fully solve prompt injection attacks but helps shrink the possible universe of attacks, preventing longer prompts that leverage e.g. repetition and sockpuppetting [[SOCKPUPPETTING]] to convince agents of malicious tasks. The specification already implements a nominal size restriction of 128 characters for the tool {{ModelContextTool/name}} (see [[#supporting-concepts]]), but further work is needed to evaluate the right size limits for titles, names, and other inputs. See [Issue #73](https://github.com/webmachinelearning/webmcp/issues/73). +**How:** This restriction would not fully solve prompt injection attacks but helps shrink the possible universe of attacks, preventing longer prompts that leverage e.g. repetition and sockpuppetting [[SOCKPUPPETTING]] to convince agents of malicious tasks. The specification already implements a nominal size restriction of 128 ASCII [=code points=] for the tool {{ModelContextTool/name}} (see [[#supporting-concepts]]), but further work is needed to evaluate the right size limits for titles, names, and other inputs. See [Issue #73](https://github.com/webmachinelearning/webmcp/issues/73).

Supporting interoperable probabilistic defense structures through shared attack eval datasets