Solana Smart Contract Vulnerabilities

• Solana smart contracts are on-chain executable programs that manage decentralized applications through explicit account handling on a high-throughput blockchain.
• Vulnerabilities result from verification omissions, arithmetic errors, and cross-program invocation flaws, often stemming from design oversights and operational risks.
• Security research employs static, dynamic, and machine learning-based analyses to detect and mitigate risks, fostering robust tools and standardized vulnerability taxonomies.

Solana smart contracts—often termed "programs"—are on-chain executable entities that interact with account-based state under the high-throughput, message-oriented runtime of the Solana blockchain. While enabling scalable decentralized applications, Solana smart contracts exhibit a specific spectrum of security vulnerabilities distinct from and overlapping with those on platforms such as Ethereum. These vulnerabilities arise from coding errors, design omissions, ecosystem-specific runtime features, and operational factors across deployment and governance. Detection and mitigation methodologies are evolving rapidly, with tool chains and frameworks in the Solana ecosystem maturing to address these risks.

1. Principal Vulnerability Types in Solana Smart Contracts

Solana smart contract vulnerabilities can be taxonomized according to several categories:

1. Verification Omissions
   • Missing Signer Check: Contracts that fail to verify whether the expected signer actually signed the transaction allow unauthorized parties to perform privileged operations. For example, if an admin update relies only on parameter checks but not signer validation, attackers may escalate privileges (Wu et al., 10 Apr 2025).
   • Missing Ownership Check: If the contract does not validate the owner field (i.e., account program ownership), forged or malicious accounts may be passed to critical operations such as asset withdrawal (Wu et al., 10 Apr 2025).
   • Rent-Exemption Omission: Failure to ensure accounts are rent-exempt can lead to account eviction and unintended state loss during execution.
2. Calculation and Arithmetic Faults
   • Integer Overflow/Underflow: Arithmetic errors such as unchecked addition or multiplication may result in asset misallocation. Consider $amount = n \times v$, where setting $n = 2$, $v = 2^{255}$ yields $2^{256}$, exceeding the $2^{256} - 1$ bound of uint256 and causing a wrap to zero—thereby bypassing checks like $require(balance \geq amount)$ (Wu et al., 10 Apr 2025).
   • Numerical Precision Errors: Poor handling of floating-point arithmetic in AMM or financial protocols may enable small exploits detectable only under adversarial input patterns.
3. Cross-Program Invocation and Data Format Attacks
   • Arbitrary Cross-Program Invocation (ACPI): Contracts that invoke external programs without validating their identity or ownership (via program id or owner checks) allow attackers to redirect calls to malicious targets (Andreina et al., 19 Jun 2024, Smolka et al., 2023).
   • Solana Account Confusion: The loose type system permits ambiguous handling of account data, potentially conflating program and data accounts or enabling stale data exploitation upon contract upgrades.
4. State-Related and Reinitialization Flaws
   • Re-initialization / Cross-Instance Confusion: Residual state across contract upgrades or reinitializations may be manipulated for unauthorized changes if not properly zeroed or validated.
5. Off-Chain and Operational Risks
   • Key Leakage / Entropy Deficiency: Weak or reused keys (especially for upgrade authority or program admin accounts) may be brute-forced or phished, precipitating systemic failures (Wu et al., 10 Apr 2025).
   • Outdated Dependency Exploitation: The use of insecure or unpatched libraries can provide adversaries with indirect points of attack.
6. Logic-Level Vulnerabilities
   • Sandwich Attacks: Manipulation of transaction order in DeFi protocols to profit from transient price changes.
   • Oracle Manipulation: Untrustworthy external data sources compromise asset pricing or execution logic; mitigated in practice via M-of-N reporting or TWAP.

2. Security Analysis Methodologies and Tooling

Security analysis on Solana combines rigorous static, dynamic, and symbolic techniques, often adapted from the Ethereum ecosystem but tuned to the Rust-based, account-centric execution model.

Method	Typical Tool (Solana/Ethereum)	Detection Focus
Static Analysis	Blockworks Checked Math, cargo-audit / Slither, Semgrep	Arithmetic, dependency, code patterns
Dynamic Analysis	FuzzDelSol (Smolka et al., 2023) / ContractFuzzer, ReGuard	Runtime exceptions, input validation
Symbolic Execution	Custom Solana symbolic engine / Mythril	Path predicates, invariant enforcement

Static Analysis leverages AST and IR representations (e.g., SlithIR) to detect lexical, data-flow, and pattern-based vulnerabilities. For Solana, data-flow tracking is critical for ownership/signer validation due to the stateless invocation model (Wu et al., 10 Apr 2025).

Dynamic/Fuzz Testing with binary-only fuzzers (FuzzDelSol) uses coverage-guided input mutation to discover bugs in deployed ELF/eBPF bytecode. Custom bug oracles detect missing signer/owner checks, ACPI, integer bugs, and lamports-based exploits. The coverage feedback formula in FuzzDelSol is $i \leftarrow (\mathit{src} + \mathit{dst}) \bmod s$, guiding transaction mutation (Smolka et al., 2023).

Symbolic Execution explores all feasible paths (branch predicates) to model the effect of both user-driven and adversarial inputs, operating over Solana’s explicit account and instruction structure. Proper abstraction of Solana’s BPF bytecode is essential for coverage.

3. Dataset Generation and Robustness in Vulnerability Detection

Improving both quantity and diversity in training datasets is essential for robust detection:

• Semantic-Preserving Code Transformation: Transform vulnerability-containing code snippets via AST manipulation and injection into all possible valid locations within benign contracts. Operators include naming-level, expression-level (operand permutation, branch swapping), and statement-level (if-to-loop conversions) transformations (Manh et al., 29 Oct 2024).
• Measured Impact: Operator-induced transformations (e.g., $Op_{if}:$ $$if(E)\ S_t\ else\ S_f \rightarrow if(\neg E)\ S_f\ else\ S_t$$) can yield false negative rates as high as 100% for tools like Slither in some reentrancy cases, indicating weaknesses in rule-based detectors.
• Implication: This suggests that detection tools for Solana would benefit from training on similarly transformed Rust/BPF code to counter evasion via syntactic variation.

4. Distinctive Aspects of Solana Compared to Ethereum

The Solana platform presents several unique security properties:

• Ecosystem Tooling: Ethereum boasts >100 security analysis tools across paradigms, whereas Solana's ecosystem is less mature (~12 tools), with an emphasis on Rust-specific analysis (cargo-audit, Blockworks Checked Math) and bytecode (SBF) handling (Wu et al., 10 Apr 2025).
• Programming Model: Solana smart contracts, compiled from Rust/C to SBF via LLVM, are highly parallel, stateless, and require explicit account parameter passing, reducing—but not eliminating—reentrancy risks compared to Ethereum’s function-based, state-holding contracts (Vani et al., 2022).
• Upgrade and Access Control: Upgrade authority centralization is a distinctive risk on Solana (Lamby et al., 2023); a compromised upgrade key enables hostile code replacement despite sound program logic.
• Framework Adoption: Widespread use of secure frameworks (e.g., Anchor, present in >88% of deployed contracts) automates common checks (signer/owner validation) and substantially reduces real-world prevalence of critical vulnerabilities (ACPI prevalence $\approx 0.2\%$) despite experimental evidence of widespread developer oversight (Andreina et al., 19 Jun 2024).

5. Detection Frameworks: Learning-Based and Rule-Based Paradigms

Emerging learning-based approaches are reshaping vulnerability detection:

• Graph-Mining and GNNs: MANDO (Nguyen et al., 2022) models contracts as heterogeneous graphs (control-flow and call graphs, with node/edge types tailored for Solana such as ACCOUNT_ACCESS, INSTRUCTION), enabling both contract-level and line-level vulnerability detection. The multi-metapath attention mechanism computes node embeddings via:

$e'^{(\phi_k)}_i = W_{(\phi_k)} e^{(\phi_k)}_i$

with metapath attention

$$a_{ij}^{(\Phi_t^{(\phi_k)})} = \mathrm{softmax}_j(\mathrm{att}([e'^{(\phi_k)}_i, e'^{(\phi_k)}_j]; \Phi_t^{(\phi_k)}))$$

• Adaptive Feature Perception Networks: AFPNet (Chen, 7 Jul 2024) dynamically extracts critical code snippets, represented by the top-$P$ CNN activations and learns relational dependencies using a multi-head attention framework:

$$C^{(j,l)} = \operatorname{ReLU}(W^{(j,l)}\cdot E_{i:i+h_l-1} + b)$$

$$\mathrm{head}_s = \operatorname{Softmax}\left(\frac{q_s k_s^T}{\sqrt{d_k}}\right) v_s$$

These models have demonstrated F1-score improvements of $6.38\%-14.02\%$ over prior approaches across diverse datasets.

6. Practical Incidents and Multi-Tier Root Causes

A systematic review of real-world attacks identifies that catastrophic financial losses ($> \$1$ B) are typically the result of “exploit chains”—sequences of misconfigurations and flaws rather than isolated bugs (Rezaei et al., 27 Jul 2025). The four-tier root-cause framework delineates: 1. Protocol design errors (unsound logic/economic incentives). 2. Lifecycle and governance (failed upgrades, admin key loss). 3. External dependencies (oracle manipulation, unsafe inputs). 4. Code bugs (reentrancy, overflow, access misconfiguration).

Solana contracts are susceptible to similar patterns, with special consideration needed for upgrade procedures, external data integrity, and operational risk in multi-program applications.

7. Codification and Standardization Efforts

Codification of vulnerabilities via a unified scheme such as "SWE-x" (Smart contract Weakness Enumeration) (Zaazaa et al., 2023) promotes cross-chain and multi-language consistency in reporting and analysis. Even if a vulnerability is discovered on Ethereum (e.g., SWE-107: Reentrancy), analogous Solana program bugs can be classified using the same taxonomy, facilitating systematic remediation and comparative paper.

Conclusion

Solana smart contract vulnerabilities span verification omissions, arithmetic and data errors, state and upgrade management flaws, and logic-level attacks, arising from both platform-intrinsic properties and shared design/authentication principles. Detection and mitigation paradigms incorporate static, dynamic, symbolic, and machine-learning approaches, further strengthened by semantic-preserving dataset expansion. The emergence of robust frameworks (Anchor), the adaptation of comprehensive analysis techniques, and the move towards unified codification systems collectively bolster Solana’s resilience, though operational, governance, and external dependency risks remain critical areas for ongoing research and practical defense (Wu et al., 10 Apr 2025, Rezaei et al., 27 Jul 2025, Brighente et al., 2022, Smolka et al., 2023, Vani et al., 2022, Nguyen et al., 2022, Zaazaa et al., 2023, Lamby et al., 2023, Andreina et al., 19 Jun 2024, Manh et al., 29 Oct 2024, Chen, 7 Jul 2024, Liang et al., 19 Aug 2024).