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Low 29 Cryptographic libraries

tx_memory_pool: speedup get_complement() for large requests

Public commit record

What the developer wrote

Authored by jeffro256

73/100 · Adequate
tx_memory_pool: speedup get_complement() for large requests

Changes complexity from M*N to (2*N+M)*log2(M). The FCMP++ stressnet recently hit mempool sizes of ~55k txs.
If the requesting node's mempool is populated, this results in an average of (55000*55000)/2
(about 1.5 billion) comparisons for the responding node. Under this commit, this would be reduced to
(55000+55000)*log2(55000) comparisons (about 2.6 million), a 99.83% reduction.
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context
The short version

What changed, and why it matters

This commit is a performance optimization for Monero's transaction memory pool. When one node asks another for transactions it is missing, the old code compared every requested hash against every pool transaction one-by-one, which becomes extremely slow when the pool is large (tens of thousands of transactions). The new code sorts the requested hashes once and uses binary search, cutting the work by roughly 99.8%. The main security-relevant angle is that the old code could be abused to make a node waste CPU time, potentially contributing to a denial-of-service condition.

Recommended action

Treat this as a worthwhile hardening/performance fix. Consider pairing it with an upper bound on the number of hashes accepted in a txpool complement request and/or per-peer rate limiting, because the optimization removes the quadratic CPU blow-up but very large requests can still consume memory and bandwidth.

Security signals we found

01

Algorithmic-complexity reduction in a network-facing RPC/P2P path

02

Old O(N^2) behavior could be triggered by large mempool sizes and used to consume CPU on responding nodes

03

No input-size limits or rate limiting are added in this patch

04

No changes to validation, cryptography, or consensus rules

Risk score

Why this scored 29/100

Our methodology →
Potential impact 5/30
Exploitability 4/25
Stealth signal 3/15
Affected reach 6/15
Confidence 7/10
Evidence quality 4/5
Human-validated context

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