CVE-2026-113729

Exclusive XML canonicalization (`http://www.w3.org/2001/10/xml-exc-c14n#`) in `github.com/moov-io/signedxml` copied the entire accumulated namespace map at every node during `ValidateReferences()`, before any reference digest or signature check. For a chain of nested elements that each declare many unused namespace prefixes, that produced **O(m × depth²)** time and peak memory (`m` = prefixes per level). `beevik/etree` caps tree depth (~1024), but `m` is unbounded, so a small attacker-controlled document (on the order of 1 MB) could drive multi-gigabyte allocations and OOM the process. No valid signature, matching certificate, or authentication is required. This is the library’s core path for SAML / WS-Federation service providers, which canonicalize attacker-supplied XML prior to authentication. ### Affected - Package: `github.com/moov-io/signedxml` - Function: `ExclusiveCanonicalization` (`exclusivecanonicalization.go`) - Verified: **v1.4.0** and current default branch (commit `d79064a`). Earlier v1.2.x–v1.3.x releases are likely affected. ### Remediation Do not copy the whole namespace map per node. Maintain a single scope and push/pop only the prefixes introduced at each element. Replace linear prefix-in-scope scans with set lookups. Impose a defensive cap on in-scope namespace declarations and return an error rather than allocating unbounded memory. From @shuji-oh ``` Thanks for the fix — I re-ran my PoC against this branch and confirmed the memory blowup is gone. Before (main) -- Doubling depth each time (m=10 fixed): allocated grows ~4x each time ≈ O(depth^2) -- depth=250 m=10 payload= 0.07MB allocated= 45MB depth=500 m=10 payload= 0.14MB allocated= 179MB depth=1000 m=10 payload= 0.27MB allocated= 711MB -- Increasing m (depth=1000 fixed): allocated scales roughly linearly with m -- depth=1000 m=5 payload= 0.16MB allocated= 377MB depth=1000 m=100 payload= 2.57MB allocated= 6278MB After (fix/exc-c14n-namespace-dos) -- Doubling depth each time (m=10 fixed): allocated grows ~4x each time ≈ O(depth^2) -- depth=250 m=10 payload= 0.07MB allocated= 2MB depth=500 m=10 payload= 0.14MB allocated= 4MB depth=1000 m=10 payload= 0.27MB allocated= 7MB -- Increasing m (depth=1000 fixed): allocated scales roughly linearly with m -- depth=1000 m=5 payload= 0.16MB allocated= 4MB depth=1000 m=100 payload= 2.57MB allocated= 47MB That's roughly a 100–130x reduction in peak allocation for the same payload sizes, and no OOM in any of my runs. This looks like it resolves the issue — thanks for the quick fix! ``` ### Credit Reported by Shuji Ohira ([@shuji-oh](https://github.com/shuji-oh)).