Ever wondered what it takes to make sure the math behind a DeFi protocol isn’t secretly bleeding users dry? Saber’s recent audit grabbed headlines and for good reason. As a key player on Solana, Saber has become synonymous with efficient stablecoin swaps, and this review didn’t just touch the surface—it dove into the heart of its StableSwap logic.
- Saber: A Solana DeFi powerhouse. Since launching in mid‑2021, Saber has carved its niche as the go‑to AMM for stablecoins and liquid‑staking tokens on Solana. With low fees, blazing fast speed, and billions in cumulative trading volume, it’s a cornerstone of the ecosystem.
- Risk ahead: why now? DeFi dramas from Wormhole to Crema exploits have shown that speed and low fees don’t equal safety. Saber’s StableSwap logic, let loose in a wild‑west environment, needed expert eyes to ensure the math held firm.
- Curve-style AMMs: the silent game-changers. Saber’s math isn’t just unique—it’s rooted in the Curve model, designed for ultra-tight stable stablecoin swaps. This approach dramatically reduces slippage compared to generic AMMs, but it’s math‑heavy and fragile making audits not just smart, but essential.
The Audit Mission – What Was at Stake
Saber’s smart contract audit wasn’t just another item on a checklist. With millions in total value locked and complex mathematical logic driving every trade, the stakes were high across several fronts.
Protecting capital in high-throughput environments
Saber handles large volumes of stablecoin transactions with minimal slippage. This efficiency relies on precise calculations within its StableSwap algorithm. Even minor errors in logic could cause disproportionate losses or open subtle attack vectors. The audit was essential to prevent value leakage or potential manipulation under stress conditions.
Confirming the accuracy of StableSwap logic
The core of Saber’s architecture is a modified version of Curve’s StableSwap formula, adapted to Solana’s performance characteristics. The audit focused on validating this formula, ensuring the amplification factor, swap invariants, and liquidity equations were implemented with mathematical correctness and consistent results under all pool conditions.
Building institutional and community confidence
In the DeFi ecosystem, transparency and trust are earned through action. By commissioning an independent audit and disclosing the results, Saber aimed to strengthen its credibility among users, developers, and institutional partners who rely on consistent behavior from smart contracts.
Key components under review
The scope of the audit covered every part of Saber’s swap engine and administrative logic:
- Invariant-based math for swaps and LP share issuance
- Fee distribution and rounding procedures
- Administrative access controls, including upgrade paths
- Pool parameter configurations and slippage bounds
Strategic impact for Curve-style protocols
Saber’s architecture is representative of a broader class of AMMs optimized for stable assets. Findings from this audit are relevant not just for Saber, but for any protocol employing similar mathematical models. A robust audit in this case contributes to improving the reliability baseline across Solana’s DeFi stack and beyond.
Audit Discoveries and Fixes
The Bramah Systems audit identified critical vulnerabilities within Saber’s StableSwap implementation, assigned severity levels, and provided actionable remediation. Here’s a thorough breakdown:
A. Key Vulnerabilities Identified
- Edge-case math convergence issues: In rare high-amplification scenarios, the invariant solver didn’t converge correctly, risking precision loss and imbalanced pools under extreme conditions.
- Overflow and rounding concerns: Some multiplication and division steps lacked sufficient precision or safe boundaries, leading to potential overflow or off-by-one errors in fee calculations—especially with large token balances.
- Fee misapplication: Poor rounding logic in fee deductions occasionally allowed unintended small profits, eroding values over multiple transactions.
- Admin & upgrade paths exposed: Certain administrative control flows (like pool parameter adjustments) lacked timelock or strict access checks, potentially enabling immediate, unauthorized changes.
B. Severity Levels
- Critical: Vulnerabilities in mathematical logic that could be exploited during swaps or LP operations, causing irreversible value extraction.
- High: Overflow and rounding issues that could erode value under normal usage.
- Medium: Administrative permission gaps that required tightened access controls.
These findings were clearly prioritized to drive rapid follow-up actions.
C. Fixes & Improvements
- Invariant solver enhancements: Adjusted loop limits, convergence thresholds, and added fallback checks to guarantee stability even under fringe parameter ranges.
- Safe math wrappers: Core operations now use overflow-resistant constructs and proper boundary checks, reducing the risk of arithmetic faults.
- Rounding standardization: Fee deduction methods are now strictly floor-rounded, avoiding dust accumulation or unexpected profits.
- Hardened admin controls: Admin-only methods now strictly enforce multi-signature (multi-sig) requirements and timelocks, preventing instant or unilateral configuration changes.
D. Validation of Remediation
- Unit-test augmentations: The audit prompted new test cases simulating aggressive amplification settings, near-zero balances, and repeated rapid swaps.
- Fuzz and symbolic testing: Automated tools probed edge cases continuously, confirming robustness under unexpected inputs.
- Formal invariant simulations: Auditors compared Saber math outputs to canonical implementations (e.g., Curve) across diverse configurations to ensure consistency
Launching soon? Ensure your code is secure.
StableSwap Mechanics – How the Math Powers the Pool
This section breaks down the core mathematical engine behind Saber’s Curve-style AMM. Each step is essential to understand how swaps work, why amplification matters, and how the algorithm maintains stability.
A. The Amplification Factor (A)
- Purpose: Amplification adjusts the bonding curve between a constant-sum model (zero slippage) and a constant-product model (higher slippage).
- Impact: A higher A value makes swaps smoother—close to 1:1 trades—but extreme imbalances trigger more product-like behavior.
- Implementation: Saber integrates A into its invariant equations and allows ramping schedules for gradual adjustments .
B. The Invariant (D) Calculation
- Definition: D represents the total “value” stored in the pool considering amplification.
- Role: During swaps or liquidity changes, D is recomputed to enforce the invariant equation. Saber mirrors Curve’s algorithm, specifically get_D() for this purpose. Industries use Newton-Raphson iterations to compute D robustly.
C. Output Amount Derivation (get_y())
- Process: After a token is added (x increases), Saber runs get_y() to solve for the new balance (y) of the other token to maintain D.
- Algorithm: This involves solving an n-dimensional polynomial using iterative methods similar to Curve’s 3pool logic. The goal is precise and deterministic token output.
D. Combined Curve Behavior: Sum + Product
- Curve dynamics: The invariant balances two forces:
- Near equilibrium: Dominated by linear (x + y) behavior—minimal slippage.
- Far from equilibrium: Transitions toward the product curve (x·y)—protects against draining.
E. Fee & Round-offs
- Fee structure: Saber deducts trading fees from output (dy) after get_y() completes, then splits those fees per protocol parameters.
- Rounding logic: To avoid tiny value leaks (“dust”) or rounding attacks, Saber floors computed outputs, delaying exposures until audit enhancements ensured safer precision handling
Performance & Gas Optimizations
Saber leverages Solana’s ultra-fast chain, but even then, performance tuning ensures cost-effectiveness, speed, and precision. This section outlines where the audit highlighted opportunities for optimization and steps taken to streamline execution.
A. Transaction Cost and Throughput in Solana
- Low-cost design built for scale: Solana processes thousands of transactions per second, yet Saber’s StableSwap logic must be lean to minimize compute units and lamport (gas) usage per swap.
- Target areas: Loops, iterative math, external calls, and state writes are the most resource-intensive — audit focused heavily here.
B. Optimizing Invariant and Swap Calculations
- Converging faster: Loop limits and thresholds in D and get_y functions were fine-tuned for quicker convergence, reducing average iteration counts without losing accuracy.
- Selective precision: Higher precision is retained near equilibrium; far from it, fewer steps are used to strike a balance between speed and accuracy — reducing computation overhead.
C. Assembly-Level Enhancements & Safe-Math Usage
- Optimized arithmetic: Critical math operations now use unchecked blocks and Rust intrinsics were safe, enabling faster multiplications/divisions.
- Batch wrapping: Aggregates update operations to reduce Solana account writes, leading to fewer cross-program calls and lower transaction costs.
D. State and Memory Management
- Account layouts: Structuring data to pack related fields minimizes account space and avoids costly re-serialization.
- Minimized borrow and copies: Audit flagged redundant borrow patterns that, once removed, significantly trimmed instruction count and transactions per swap.
E. Fuzzing & Benchmark-Based Refinements
- Automated benchmarks: Fuzz scenarios mimicking realistic swap sequences were analyzed to profile heavy paths.
- Targeted optimizations: Feedback loop resulted in reducing cost per swap by approximately 10–20% in common stablecoin use cases (e.g., USDC/USDT pools).
Security Controls & Safeguards
Smart contract security isn’t just about perfect math—it’s also about layers of control. The audit confirmed several governance and code-level defenses put in place to keep the protocol resilient and trustworthy.
A. Timelocks + Multi‑Signature for Governance
- Multi‑sig protection: Administrative actions like adjusting amplification, updating fees, or pausing contracts require multi-sig approval. This ensures no single entity holds unilateral power—deposits, swaps, and withdrawals are shielded from rogue changes.
- Time‑locked updates: Even after valid multi-sig approval, changes are subject to timelock delays. This window serves as a guardrail, giving users and developers time to react to governance proposals. The combination of both mechanisms—multi-sig and delay—drastically reduces the risk of sudden, unauthorized protocol modifications.
B. Contract-Level Assertions & Fallbacks
- Invariant guards: Critical math functions now include built-in assertions. For example, after computing block values like D and y, the contract validates that results remain within expected margins before proceeding. If invariants fail, actions revert, preventing faulty swaps.
- Emergency stop logic: Several contracts now feature pause functions that can halt swaps and liquidity operations. This allows maintainers to intervene in case of detected anomalies—before small errors escalate into broader protocol issues.
C. Secure Oracle and External Call Practices
- Oracle isolation: While Saber’s StableSwap doesn’t depend on price oracles during swaps, any external calls (e.g. cross-program interactions) are designed to be minimal, atomic, and safe.
- Limited external dependencies: Minimizing reliance on outside code reduces potential vectors for exploit chains—contract updates and state changes occur in tight, controlled flows.
D. Secure Math Execution
- Safe-math wrappers: Following the audit, all sensitive operations—including amplification adjustments and fee calculations—now use audited math libraries with overflow checks and strict value bounds.
- Assembly optimization with clarity: To keep execution efficient, some math routines were optimized using low-level intrinsics. But this was done carefully, without bypassing the safe arithmetic layers necessary to prevent overflow or precision loss.
E. Post-Audit Governance Commitments
- Public disclosure of changes: Saber published both the original audit report and subsequent patches, ensuring full transparency.
- Commitment to bug bounties: Saber supports public bounties, rewarding external researchers and reinforcing ongoing, community-driven defense.
- Upgrade-tracking dashboards: The team established real-time monitoring dashboards that track changes to critical parameters—helping users verify that pool configurations haven’t changed unexpectedly.
Conclusion
Saber’s smart contract audit highlights the depth of precision, transparency, and resilience required for stablecoin-focused DeFi protocols. From math-heavy invariant checks to admin control hardening and gas optimizations, the audit not only reinforced Saber’s security posture but also set a benchmark for Curve-style AMMs across ecosystems. With the right balance of formal verification, fuzz testing, and multi-layered safeguards, Saber’s post-audit architecture reflects the evolving standards of decentralized finance. For projects looking to match that standard, Blockchain App Factory provides end-to-end smart contract audit solutions—combining rigorous code analysis, mathematical validation, and continuous monitoring to ensure your protocol earns trust before launch and beyond.
