Failed Transactions on Solana

Updated 15 October 2025

Failed transactions on Solana are rejected submissions due to program logic errors, resource constraints, or conflicts in parallel execution.
Empirical evidence highlights that bot activities, DEX aggregator issues, and intrinsic smart contract flaws are major contributors to transaction failures.
Current research focuses on advanced mitigation strategies, including adaptive scheduling and dynamic fee structures, to enhance network performance.

A failed transaction on Solana is a submitted transaction that, after being included in a block and executed by the network’s runtime, does not achieve its intended effect—usually due to violations of program logic, resource constraints, transaction conflicts, or validation failures. The phenomenon of transaction failure on Solana has profound implications for smart contract reliability, user experience, network throughput, security, and DeFi risk, and has become a critical research area as the platform has scaled to high throughput and complex financial operations.

1. Characterization and Typology of Failed Transactions

Extensive empirical analysis of more than 1.5 billion failed transactions across 72 million Solana blocks reveals that failures are highly non-uniform in distribution, both by initiator and by the smart contract “program” responsible for the first error (Zheng et al., 25 Apr 2025). Bot accounts, which account for a substantial proportion of total attempted transactions, exhibit a failure rate of 58.43%, compared to 6.22% for human users. Certain programs are chronic sources of failures; for example, DEX aggregators (e.g., Raydium Liquidity Pool V4 and Jupiter Aggregator V6) alone account for nearly 40% of all failures on the network.

Failure types are systematically categorized from over 170 error messages into a 10-class taxonomy:

Error Type	Incidence Among Failures	Notable Source Programs
Price/profit not met	47.99%	DEX Aggregators (e.g., Jupiter)
Invalid status	19.19%	AMMs (e.g., Raydium)
Validity expiration	17.72%	Broad cross-program (esp. bots)
Invalid input account	less prevalent	Mixed
Invalid input parameters	less prevalent	Mixed
Out-of-funds	Human-initiated	Various
Program logic constraint	Broad	Custom logic
Program runtime error	Broad	Low-level code issues
Out-of-resource (Compute)	Network congested periods	High bot/traffic periods
(Other, uncategorized)	Residual	-

Temporal autocorrelation in the failure rate demonstrates strong periodicity, matching high network usage times and implicating bot-induced traffic bursts as a primary macro-driver of failed transactions.

A notable statistical pattern is that failed transactions tend to occupy deeper positions within blocks (median block rank 592 for failed, vs. 529 for successful), often pay higher fees per compute unit, but are less resource-intensive in execution—indicative of systemic resource competition and scheduling inefficiencies (Zheng et al., 25 Apr 2025).

2. Root Causes: Conflict Dynamics and Parallel Execution

Solana’s high-performance ambition is realized via an explicit read–write aware parallel execution model, in which each transaction declares in advance its read and write set. The SeaLevel runtime statically analyzes these sets to extract a conflict graph, enabling parallel execution of transactions absent dependency, and serializing those that conflict (Anjana et al., 8 May 2025).

Quantitative analysis finds Solana blocks are characterized by long sequential chains of transaction dependencies: the longest such chain averages 59% of block size (vs. 18% for Ethereum), and only ~4% of recent non-voting transaction sets are conflict independent. Most conflicts are write–write, with a formal definition:

$\text{Conflict}(T_i, T_j) \iff (Wset_{i} \cap Wset_{j}) \cup (Wset_{i} \cap Rset_{j}) \cup (Rset_{i} \cap Wset_{j}) \ne \emptyset$

This dense dependency structure constrains maximum theoretical parallelism per block and increases the window for state divergence, leading to higher transaction abort and failure rates. Recent empirical periods show non-voting transaction success rates dropping to 54–62% (from ~85% historically), mainly attributed to this conflict bottleneck (Anjana et al., 8 May 2025).

Mitigation strategies under consideration include more accurate dynamic conflict detection, multi-version concurrency control, and the development of adaptive scheduling algorithms that can more effectively partition conflicting “families” (Anjana et al., 8 May 2025).

3. Programmatic and Smart Contract-Level Failure Sources

At the smart contract level, failure arises from several persistent vulnerabilities (Smolka et al., 2023, Wu et al., 10 Apr 2025):

Lacking Signer/Ownership Checks: Absence of signature or ownership validation permits unauthorized operations, leading to transaction rejection or state corruption.
Rent-Exemption Omission: If a required account is not rent-exempt, it may be evicted, leading to data loss or failed updates.
Account Confusion and Invariant Violations: Indistinct checks on account types or structure allow stale or forged account usage.
Arithmetic Errors: Integer overflow or underflow, for instance via unchecked operations $amount = cnt \times \_value$ (with large values), can bypass balances or write invalid state, potentially enabling fund misappropriation or failed settlement.
Cross-Program Invocation (CPI) Risks: Failure to validate target program identity in CPI allows attackers to redirect calls, increasing the failure rate via logic errors.
Oracle Attacks and Timing Asymmetries: Improper handling of oracles or external data feeds can result in transactions executing on manipulated data, causing reverts or significant fund loss.

While Ethereum enjoys a mature static/dynamic analysis tool ecosystem (Mythril, Slither, Securify), Solana’s toolchain is still growing, with a tilt towards fuzzing and customized symbolic analysis on Rust-based contracts (Smolka et al., 2023, Wu et al., 10 Apr 2025). Tools like FuzzDelSol highlight that missing identity checks and arithmetic vulnerabilities are among the most frequent triggers of failed transactions (Smolka et al., 2023).

4. Abuse Patterns: Bots, Front-Runners, and Exploits

Bot spamming is a primary driver of transaction failure, contributing to 58.43% failed rate among automated transactions (Zheng et al., 25 Apr 2025). One driver is DEX and AMM arbitrage, where bots aggressively submit high volumes of “sniper” or sandwich transactions, each making speculative attempts to capture favorable price movement, leading to “price or profit not met” and “invalid status” errors.

Phishing attacks exploit Solana’s account and transaction structure in novel ways (Li et al., 7 May 2025): multi-transfer transactions, account authority transfers, and system address impersonation all manifest as failed or successfully malicious transactions. The empirical paper of SolPhishHunter uncovered $1.1M in losses across 8,058 phishing transactions, with attacks continually evolving in method.

Rug pull events, where DeFi project developers drain liquidity pools, are another source of abrupt failures affecting large-scale user participation and liquidity (Alhaidari et al., 6 Apr 2025). Here, failed user withdrawals post-drain are a primary symptom. The SolRPDS dataset reveals that inactive liquidity pools show abnormal “add-to-remove” liquidity ratios, averaging 6.88×10⁴, and the majority of inactive (rugged) tokens are “1-day” short-lived tokens.

5. System-Level and Consensus Mechanism Factors

Solana’s consensus model hinges on Proof of History (PoH) to produce a verifiable sequential ordering of transactions. The recursive construction,

$H_{i+1} = \text{sha256}(H_i), \quad H_n = \text{sha256}(H_{n-1}, T)$

enforces deterministic event ordering. Combined with proof-of-stake (PoS) weighted voting, this enables high throughput and rapid transaction finality (Song et al., 7 Apr 2024, Li et al., 2022). Batch reconciliation and leader-verifier separation localize the impact of single-fault transactions. If a leader malfunctions or injects errors, rapidly rotating the leader node and distributed batch validation localize and mitigate the propagation of failures.

Nonetheless, this architecture exposes additional bottlenecks: the sequential generation step by the leader prohibits parallelization at that point, and during periods of network congestion, incorrect time ordering or validator asynchrony can trigger block rejections, resulting in valid transactions being lost or delayed (Song et al., 7 Apr 2024).

Built-in mechanisms (e.g., multiple verifiers, batch reconciliation, periodic leader rotation) contribute to the isolation and rapid recovery of failed transaction episodes, supporting the system’s high aggregate reliability (Li et al., 2022).

6. Transaction Monitoring, Runtime Verification, and Detection

Advanced approaches for runtime verification of transaction semantics, such as transaction monitoring via the augmentation of smart contracts with monitor functions (A_init, A_begin, A_end, A_term), can address application-level failures not covered by low-level resource or scheduling checks. Enhancing the Solana runtime with instructions such as “first” (first invocation in transaction), “fail” (per-contract abort flag), and “transaction memory” enables property enforcement across entire transaction lifecycles (Capretto et al., 2022).

Runtime verification is critical in enforcing invariants such as flash loan repayment within a transaction, thereby preventing failures due to multi-contract interactions. However, current blockchains, including Solana, are limited by execution models that operate in local operational contexts (BFS/DFS) and lack the transaction-scope atomic monitoring semantics necessary to prevent high-level invariant violations (Capretto et al., 2022).

Recent research also demonstrates the efficacy of binary-only, coverage-guided fuzzing architectures (e.g., FuzzDelSol) that integrate coverage feedback and taint tracking, enabling the identification of vulnerabilities with high precision and offering reproducible failing transactions for audit and remediation (Smolka et al., 2023).

7. Mitigation Strategies and Research Directions

A suite of countermeasures, both systemic and programmatic, is under investigation:

Dynamic Fee Structures: Implementing fees that adapt to submission frequency or congestion to disincentivize bot flooding while maintaining usability for genuine users (Zheng et al., 25 Apr 2025).
Dependency and Priority-Aware Scheduling: Introducing explicit dependency checks before ordering (formally, for any in-flight transaction $Tx_m$ , defer $Tx_n$ if $D(Tx_m, Tx_n) = 1$ ), and employing transaction priority functions to resolve scheduling, can reduce conflict-induced failures (Bappy et al., 30 Jul 2024).
Granular Resource Prediction Tools: Developing accurate compute unit estimators and fee calculators to reduce programmatic failure due to resource misestimation (Zheng et al., 25 Apr 2025).
Enhanced Static/Dynamic Analysis Tools: Continued development of fuzzing, symbolic execution, and hybrid analysis (especially for bytecode, given the low rate of open source contracts on Solana) remains crucial (Smolka et al., 2023, Wu et al., 10 Apr 2025).
ML and Heuristic-Based Real-Time Detection: Using labeled datasets (e.g., SolRPDS) and ensemble learning methods to preemptively flag liquidity withdrawal anomalies and rug pulls, thus reducing end-user failure rates and fraud (Alhaidari et al., 6 Apr 2025).

In the long run, the research agenda includes adaptive multi-version data structures for concurrency management, runtime verification frameworks for enforceable application invariants, and full integration of transaction monitoring primitives into the Solana execution model (Capretto et al., 2022, Anjana et al., 8 May 2025).

Failed transactions on Solana are a composite phenomenon rooted in conflict-laden parallel execution, smart contract vulnerability, programmatic and economic exploits, systemic high-load scheduling, and limited transaction-scope monitoring. While Solana’s architecture supports high throughput and rapid block production, these very features introduce unique stressors and failure modalities. Addressing these challenges is an active area of research, integrating empirical analysis, advanced static and dynamic analysis toolchains, runtime verification, and economic as well as algorithmic mitigation. As throughput and on-chain financial complexity grow, systematic improvement of failure handling remains a prerequisite for the reliability and maturity of the Solana ecosystem.