Round 1 Q1 2025

Hash 631f5125b844546fd1c214d1dfda939e9c1c08da

OxMacro

Ammalgam-1 Preliminary Audit Report (external)

Chain Security

ChainSecurity_Ammalgam_DLEX_audit_draft.pdf

Fixes Round 1

Response_ChainSecurity_Ammalgam_DLEX_audit.pages

Round 2 Q3 2025

Cantina Audit Competition

report-competition-ammalgam.pdf

attachment:b5f441d4-0b38-4fc8-a2f0-b6b9ea4b9591:report-competition-ammalgam.pdf

Chain Security Manual Audit

ChainSecurity_Ammalgam_DLEX_audit_V2_draft.pdf

attachment:b4acd136-dd8d-4bf0-a787-1345ff14f5a7:ChainSecurity_Ammalgam_DLEX_audit_V2_draft.pdf

Round 2 Fixes

RELEASE NOTES

Claude fix Review Cantina

Claude ChainSecurity Fix Review

Round 3 Q1 2026 (Fixes in progress)

Phil Manual Audit

Phil was second place in the Cantina Comp

https://gist.github.com/philbugcatcher/6263aaf6d6eddbbb42c44dfe34d1f544

Pruvendo Formal Verification Saturation Library

The Saturation library had the lowest finding per line of code and so we engaged with a formal verification team to focus specifically on this library (half the code base) due to the complexity.

Pruvendo added Lean formal verification for the library and added 2,313 new tests, which included both invariant and fuzz tests.

Pruvendo: formal verification and security audit suite
Comprehensive fuzz testing, formal verification, and security audit
for Ammalgam protocol, developed by Pruvendo.

Test suite (2313 tests):
- test/spec/: 2001 tests across 15 modules (Saturation, Liquidation,
  GeometricTWAP, Validation, Interest, Pair, Convert, TickMath,
  QuadraticSwapFees, DebtToken, TokenController, Constants,
  TokenSymbol, Proxy, Uint16Set)
- test/audit/: 305 tests (findings PoC + QC investigations)
- 870 formalized invariants (INV-XXX markers)
- 225 reference implementations (REF-XXX markers)

Lean 4 formal specification:
- Definitions: Saturation, Liquidation, Pricing, Solvency
- Proofs: SaturationProofs (20 files), LiquidationProofs (10 files),
  LiquidationBoundaryProofs, PricingProofs, SolvencyProofs

Security findings (22 total):
- 6 FIXED: AUDIT-005, 006, 012, 013, 014a, 014c
- 6 CONFIRMED (open): AUDIT-015, 016, 017, 018, 019, 021
- 1 OPEN (defense-in-depth): AUDIT-014b
- 2 FUNCTION-ONLY: AUDIT-004, 020
- 1 DESIGN ISSUE: AUDIT-001
- 1 BY DESIGN: AUDIT-002
- 6 NOT A BUG: AUDIT-003, 007, 008, 009, 010, 011

AI Scanners

It is important to note that AI scanners can have a high false positive rate. We have used our own internal tools to eliminate 80% or more of these findings as false positives or duplicates, and we continue to review the findings to identify potential issues. However, many issues found in our manual review and formal verification were also caught by these tools as well.