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Home Chronicles FaultSeeker: LLM-Empowered Blockchain Fault Localization APT Analysis Threat Intelligence Nation-State Threats LLM Security Prompt Injection Generative AI Web3 Security Blockchain Security
FaultSeeker: LLM-Empowered Blockchain Fault Localization Feng Ning (风宁)
Efficiency Revolution : Compresses 16.7-hour manual analysis to 4-8 minutes, achieving 115-227x speedupCost Optimization : $1.55-$4.53 per transaction, reducing costs by 99.7% compared to manual analysisCognitive Innovation : Leverages human memory and attention mechanisms in two-stage analytical architectureMulti-Agent Collaboration : Orchestrator coordinates specialist agents for deep reasoning and cross-validationReal-World Validation : Outperforms existing tools on 115 real malicious transactions, achieving production-ready level Chapter 1: Blockchain Security Challenges and Technical Opportunities 1.1 DeFi Security Threat Landscape Web3 applications face unprecedented security challenges in 2025. According to industry analysis, smart contracts hold over $100 billion in locked value, yet security vulnerabilities caused $1.42 billion in losses in 2024. Major attack types include:
Reentrancy Attacks : Exploit contract call timing vulnerabilities for repeated asset extraction (classic case: The DAO incident)Flash Loan Attacks : Uncollateralized lending + price manipulation combinations, causing tens of millions in single-incident lossesPrice Oracle Manipulation : Manipulating price feed data sources to trigger erroneous contract decisionsCross-Chain Bridge Vulnerabilities : Multi-chain interaction verification flaws, becoming the largest attack surface (accounting for over 40% of 2024 losses)Access Control Flaws : Incorrect access control logic leading to unauthorized operations Traditional fault analysis relies on manual security expert work:
On-Chain Data Collection : Tracking transaction traces from block explorers (1-2 hours)Contract Code Review : Decompiling bytecode, understanding business logic (4-6 hours)Attack Path Reconstruction : Simulating attack flow, locating root cause (6-8 hours)Report Writing : Organizing evidence chain, proposing remediation (2-3 hours)
⏱️ Poor Timeliness : Average 16.7 hours, missing golden response window
💰 High Costs : At $100/hour, single analysis costs $1,670
📈 Not Scalable : Expert resources scarce, unable to batch-process historical cases
🎯 Weak Consistency : Different analysts may reach divergent conclusions
1.2 LLM Breakthroughs in Code Analysis Large Language Models (LLMs) demonstrate powerful code comprehension capabilities, providing technical foundation for automated security analysis:
Semantic Understanding : Not just syntax patterns, but also business logic intentCross-Contract Correlation : Tracking complex multi-contract interaction chainsVulnerability Pattern Generalization : Learning from known cases to identify attack variantsNatural Language Explanation : Generating human-readable analysis reports, lowering comprehension barriers
Context Window Constraints : Single LLM struggles with complete DApp analysis (involving dozens of contracts)Hallucination Issues : May fabricate non-existent vulnerabilities or miss real threatsLack of Professional Depth : General-purpose models have insufficient understanding of blockchain-specific attack vectors FaultSeeker's Innovation : Through multi-agent collaboration + cognitive architecture design, overcomes single-LLM limitations to achieve expert-level fault localization capabilities.
Chapter 2: FaultSeeker Technical Architecture Deep Dive 2.1 Cognitive Science-Inspired Two-Stage Design Theoretical Foundation: COLMA Cognitive Layered Memory Architecture FaultSeeker's design is deeply inspired by COLMA (Cognitive Layered Memory Architecture) theory in cognitive science. COLMA is an advanced cognitive architecture model proposed in September 2025 (arXiv:2509.13235), simulating human experts' memory and attention mechanisms:
Human Cognitive Layer AI Architecture Mapping FaultSeeker Implementation
────────────────────── → ─────────────────────── → ──────────────────────────
Perception Layer → Input Processing → Blockchain Data Collection
Working Memory → Context Management → RAG + Dynamic Loading
Attention Mechanism → Focus Selection → Stage 1 Transaction Forensics
Long-term Memory → Knowledge Base → Expert Agent Pre-trained Knowledge
Reasoning Layer → Synthesis Decision → Orchestrator Coordination & Synthesis
Why Two-Stage Works (COLMA Theory Support):
Attention Mechanism :
Human experts first conduct "global scanning" (Attention) at crime scenes, not immediate deep dives
FaultSeeker Stage 1 simulates this process: rapidly traversing entire transaction graphs, identifying anomalous patterns
Theory Support: COLMA proves attention pre-filtering improves subsequent reasoning efficiency by 3-5x
Working Memory :
Human working memory capacity is limited (Miller's Law: 7±2 items), requiring batch information loading
FaultSeeker Stage 2 dynamically loads critical contract code via RAG, avoiding context overflow
Theory Support: COLMA's layered memory management reduces 40% cognitive load in complex tasks
Long-term Memory Retrieval :
Human experts automatically invoke relevant domain knowledge when encountering specific problems
FaultSeeker's expert agents pre-load specific attack type knowledge (e.g., "Flash Loan Expert")
Theory Support: Domain-specialized memory retrieval achieves 25-30% higher accuracy than general retrieval
Sustained Reasoning :
Complex cases require task forces' sustained hours/days of collaborative reasoning
FaultSeeker coordinates iterative analysis by multiple experts through orchestrator, cross-validating conclusions
Theory Support: COLMA multi-agent collaboration outperforms single models by 15-20% in fault diagnosis tasks
FaultSeeker achieves 91% accuracy on 115 cases vs. single LLM's 79-82%
Proves effectiveness of COLMA-inspired two-stage architecture in blockchain security analysis
Provides practical paradigm for "how cognitive architecture guides AI system design"
FaultSeeker borrows from human expert analysis workflows, implementing dual mechanisms of memory and attention:
┌─────────────────────────────────────────────────────────┐
│ FaultSeeker Architecture │
├─────────────────────────────────────────────────────────┤
│ │
│ Stage 1: Transaction-Level Forensics │
│ ┌──────────────────────────────────────────────┐ │
│ │ • Global Scan: Analyze blockchain tx graph │ │
│ │ • Pattern Recognition: Detect anomalous fund │ │
│ │ flows, Gas consumption patterns │ │
│ │ • Initial Screening: Locate suspicious txs │ │
│ │ and related addresses │ │
│ └──────────────────────────────────────────────┘ │
│ ↓ (Attention Mechanism - Focus on Key Info) │
│ │
│ Stage 2: Coordinated Specialist Agents │
│ ┌──────────────────────────────────────────────┐ │
│ │ Orchestrator │ │
│ │ ┌────────────┬────────────┬────────────┐ │ │
│ │ │ Reentrancy │ FlashLoan │ Price │ │ │
│ │ │ Expert │ Expert │ Oracle │ │ │
│ │ │ │ │ Expert │ │ │
│ │ └────────────┴────────────┴────────────┘ │ │
│ │ ┌────────────┬────────────┐ │ │
│ │ │ Access │ Logic │ ... │ │
│ │ │ Control │ Flaw │ │ │
│ │ │ Expert │ Expert │ │ │
│ │ └────────────┴────────────┘ │ │
│ │ ↓ (Working Memory - Iterative Reasoning) │ │
│ │ Synthesis → Root Cause → Evidence Chain │ │
│ └──────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────┘
Stage 1: Strategic Scoping
Analogous to a human expert's "first impression scan" at a crime scene:
# Stage 1 Pseudocode (Transaction-Level Forensics)
class TransactionForensics:
def analyze(self, transaction_hash):
# 1. Extract transaction metadata
tx_data = blockchain.get_transaction(transaction_hash)
# 2. Build interaction graph
interaction_graph = self.build_interaction_graph(tx_data)
# Involved addresses: [0xAttacker, 0xVictim, 0xFlashLoanProvider, ...]
# Call chain: Attacker → FlashLoan → Victim.withdraw() → ...
# 3. Anomaly pattern detection
anomalies = []
if self.detect_unusual_gas_pattern(tx_data):
anomalies.append("High Gas consumption (flash loan signature)")
if self.detect_rapid_fund_flow(interaction_graph):
anomalies.append("Rapid multi-hop fund transfers")
if self.detect_reentrancy_pattern(tx_data.logs):
anomalies.append("Suspected reentrancy call sequence")
# 4. Identify key contracts
suspicious_contracts = self.identify_key_contracts(
interaction_graph, anomalies
)
return {
"focus_contracts": suspicious_contracts,
"attack_indicators": anomalies,
"evidence_snapshot": interaction_graph
}
Graph Analysis : Construct transactions as directed graphs, identify anomalous topological structuresStatistical Anomaly Detection : Compare against historical baselines, discover behaviors deviating from normal patternsHeuristic Rules : Integrate known attack signatures like OWASP Smart Contract Top 10 Stage 2: Sustained Deep Reasoning
Analogous to a task force where multiple experts collaboratively analyze a case:
# Stage 2 Pseudocode (Coordinated Specialist Agents)
class OrchestratorAgent:
def __init__(self):
self.experts = {
"reentrancy": ReentrancyExpert(),
"flashloan": FlashLoanExpert(),
"price_oracle": PriceOracleExpert(),
"access_control": AccessControlExpert(),
"logic_flaw": LogicFlawExpert()
}
def coordinate_analysis(self, forensics_result):
# 1. Task assignment
tasks = self.plan_analysis(forensics_result)
# E.g., High Gas + rapid fund flow → assign to flashloan expert
# Repeated call pattern → assign to reentrancy expert
# 2. Parallel expert analysis
expert_findings = {}
for task in tasks:
expert = self.experts[task.expert_type]
result = expert.analyze(
contracts=forensics_result["focus_contracts"],
context=forensics_result["evidence_snapshot"]
)
expert_findings[task.expert_type] = result
# 3. Synthesis reasoning
root_cause = self.synthesize(expert_findings)
# Cross-validation: flashloan expert finds lending + price_oracle expert finds price anomaly
# → Synthesize as "flash loan + price manipulation combo attack"
# 4. Generate report
report = self.generate_report(
root_cause=root_cause,
evidence_chain=expert_findings,
recommendations=self.propose_fixes(root_cause)
)
return report
class FlashLoanExpert:
"""Flash Loan Attack Specialist Agent Example"""
def analyze(self, contracts, context):
# LLM prompt engineering
prompt = f"""
You are a blockchain security expert specializing in flash loan attack analysis.
Analyze the following smart contract code:
{contracts}
Transaction context:
{context}
Please answer:
1. Is there flash loan borrowing behavior? (Check Aave/Uniswap flash loan interface calls)
2. Are borrowed funds used for price manipulation? (Examine DEX trades, oracle calls)
3. How does the attacker profit from price deviation? (Analyze arbitrage logic)
4. Provide detailed evidence chain (call sequences, state changes, fund flows)
"""
analysis = llm.generate(prompt)
return {
"confidence": 0.95, # Score based on evidence strength
"finding": "Flash loan attack confirmed",
"evidence": analysis
}
Expert Division of Labor : Each agent focuses on specific attack types, achieving deeper analysis than general-purpose modelsCross-Validation : Multiple experts analyze independently, synthesizing judgments through voting or evidence strengthExplainability : Each expert provides independent reasoning chains, making reports audit-traceableDynamic Scalability : Can add experts based on new attack types (e.g., "Cross-Chain Bridge Expert", "MEV Expert") 2.2 Technical Comparison with Existing Solutions Technical Dimension Comparison | Dimension | DAppFL | General LLM (GPT-4o) | FaultSeeker |
|-----------|---------|----------------------|-------------|
| Analysis Method | Rule Engine | Single Model Inference | Multi-Agent Collaboration |
| Semantic Understanding | ❌ Syntax patterns only | ✅ Strong semantic understanding | ✅ Domain-specialized semantics |
| Deep Analysis | ❌ Shallow detection | ⚠️ Context-limited | ✅ Expert-level depth |
| Novel Attacks | ❌ Requires manual rule updates | ⚠️ Limited generalization | ✅ Rapid adaptation |
| Explainability | ✅ Transparent rules | ⚠️ Black-box reasoning | ✅ Complete evidence chain |
| Cost Efficiency | Very Low ($0.01) | Medium ($2-5) | Low ($1.55-4.53) |
| Time Efficiency | Seconds | Minutes | 4-8 minutes |
| Accuracy | Moderate | Good | Excellent |
Based on ASE 2025 paper experimental data, detailed comparison of FaultSeeker vs. DAppFL and other solutions:
| Performance Metric | DAppFL | GPT-4o | Claude 3.7 | DeepSeek R1 | FaultSeeker |
|-------------------|--------|--------|-----------|-------------|----------------|
| Root Cause Accuracy | 68% | 79% | 82% | 76% | 91% ⭐ |
| Attack Path Completeness | 52% | 74% | 78% | 71% | 88% ⭐ |
| False Positive Rate | 15% | 8% | 7% | 9% | 4% ⭐ |
| False Negative Rate | 32% | 21% | 18% | 24% | 9% ⭐ |
| Average Analysis Time | 3-5 min | 8-12 min | 9-13 min | 10-15 min | 4.4-8.6 min |
| Single Analysis Cost | $0.01 | $2-5 | $3-6 | $1.5-3.5 | $1.55-4.53 |
Significant Accuracy Advantage :
FaultSeeker accuracy is 23% higher than DAppFL (91% vs 68%)
9% higher than best single LLM (Claude 3.7) (91% vs 82%)False positive rate only 4%, 73% lower than DAppFL (4% vs 15%)
DAppFL Limitation Analysis :
High False Positives : Rule engine overfits historical patterns, sensitive to novel attack variantsHigh False Negatives : 32% false negative rate stems from inability to cover complex business logic vulnerabilitiesApplicable Scenarios : Quick initial screening for simple attack types (e.g., known reentrancy patterns)
Cost-Efficiency Balance :
FaultSeeker costs 155-453x more than DAppFL, but accuracy improves 34%
For high-value DeFi protocols (TVL>$100M), avoiding single attack loss (average $10M+) far exceeds cost difference
FaultSeeker's Differentiated Value :
Smarter than DAppFL : Handles complex logic vulnerabilities beyond rule engine coverage, false negative rate reduced by 72%More Professional than General LLMs : Domain knowledge injection + multi-expert collaboration, accuracy improved 9-21%, false positive rate reduced 43-56%More Efficient than Manual Analysis : 115-227x speedup, 99.7% cost reduction 2.3 Key Implementation Challenges Challenge 1: Context Window Management A single DeFi attack may involve dozens of contracts (tens of thousands of lines of code), exceeding LLM context limits.
RAG (Retrieval-Augmented Generation) : Load only code snippets relevant to current analysis taskSummary Compression : Generate summaries for non-critical contracts, retain key function detailsHierarchical Analysis : Macro-level interaction understanding first, then micro-level critical function analysis Challenge 2: LLM Hallucination Control LLMs may fabricate non-existent vulnerabilities or misinterpret code logic.
Multi-Expert Voting : Require at least 2 experts to independently confirm before marking as high-confidence findingStatic Analysis Assistance : Combine with deterministic checks from tools like SlitherConfidence Scoring : Explicitly tag analysis result reliability (0-1 score) LLM API calls are expensive; how to ensure analysis quality while controlling costs?
Tiered Analysis : Simple cases use smaller models (GPT-3.5), complex cases use larger models (GPT-4)Caching Mechanisms : Reuse historical analysis results for similar attack patternsParallelization Optimization : Execute expert agents in parallel, reducing total time FaultSeeker was validated on an extended version of the UMBC MOTIF dataset:
Scale : 115 real malicious transaction casesSource : Ethereum mainnet public security incidents (2020-2025)Type Coverage :
Reentrancy attacks: 23 cases
Flash loan attacks: 31 cases
Price oracle manipulation: 18 cases
Access control flaws: 15 cases
Cross-contract logic vulnerabilities: 28 cases
✅ Authenticity : Not laboratory-synthesized, but actual loss-causing attacks
✅ Diversity : Covers mainstream DeFi protocols (Uniswap, Aave, Compound, etc.)
✅ Complexity : Includes multi-stage combination attacks, representative
| Solution | Average Analysis Time | vs Manual Improvement |
|----------|----------------------|----------------------|
| Manual Analysis | 16.7 hours | - |
| DAppFL | 3-5 minutes | 200-334x |
| GPT-4o (Single Model) | 8-12 minutes | 83-125x |
| FaultSeeker | 4.4-8.6 minutes | 115-227x |
FaultSeeker slightly slower than DAppFL (due to depth analysis), but significantly higher accuracy
Faster than single GPT-4o (parallel multi-expert + task division efficiency advantage)
Reaches "fast enough" critical point: completes analysis minutes after incident, supporting rapid response
| Solution | Cost per Analysis | vs Manual Savings |
|----------|------------------|------------------|
| Manual Analysis | ~$1,670 | - |
| DAppFL | ~$0.01 | 99.999% |
| FaultSeeker | $1.55-$4.53 | 99.7-99.9% |
| GPT-4o (Single Model) | $2-5 | 99.7% |
ROI Case Study :
Annual security budget analysis for a DeFi protocol:
Scenario: Average 10 security incidents requiring deep analysis per year
Traditional approach cost:
- Manual analysis: 10 × $1,670 = $16,700
- Time cost: 10 × 16.7h = 167 analyst hours
FaultSeeker approach cost:
- Direct cost: 10 × $4.53 = $45.3
- Time savings: 167h - 1.4h = 165.6h freed for proactive defense
Annual savings: $16,654 + 165.6h analyst time
Additional value: Faster response avoiding loss escalation (unquantifiable but potentially millions)
Performance on 115 cases:
| Metric | DAppFL | GPT-4o | Claude 3.7 | DeepSeek R1 | FaultSeeker |
|--------|--------|--------|-----------|-------------|----------------|
| Root Cause Localization Accuracy | 68% | 79% | 82% | 76% | 91% |
| Attack Path Reconstruction Completeness | 52% | 74% | 78% | 71% | 88% |
| False Positive Rate | 15% | 8% | 7% | 9% | 4% |
| False Negative Rate | 32% | 21% | 18% | 24% | 9% |
Significantly Surpasses Rule Engine : DAppFL 15% false positives (rule overfitting), 32% false negatives (unknown patterns)Outperforms Single LLM : Multi-expert cross-validation reduces false positives (4% vs 7-9%)Approaches Human Expert Level : 91% accuracy (human experts ~95%, considering subjective variance) 3.3 Detailed Case Analysis Case 1: Cream Finance Flash Loan Attack (August 2021)
Loss: $18.5 million
Attack Type: Flash loan + price oracle manipulation
Complexity: Involves 7 smart contracts, 15 on-chain transactions
FaultSeeker Analysis Process :
Stage 1 - Transaction-Level Forensics (1.2 minutes) :
Detected anomalies:
✓ High Gas consumption (4.8M gas, normal tx <300K)
✓ Single flash loan borrowing 2,804 ETH (from Aave)
✓ Rapid DEX transaction sequence (6 Uniswap V2 txs in same block)
✓ Abnormal price fluctuation (crETH price plunged 40% briefly)
Key contracts identified:
- 0x2db0E83599a91b508Ac268a6197b8B14F5e72840 (Attacker contract)
- 0x8C3B7a4320ba70f8239F83770c4015B5bc4e6F91 (Cream crETH)
- 0x7d2768dE32b0b80b7a3454c06BdAc94A69DDc7A9 (Aave flash loan pool)
Stage 2 - Coordinated Expert Analysis (3.8 minutes) :
[FlashLoan Expert] Analysis result:
- Confirmed Aave flash loan borrowing 2,804 ETH
- Repayment verification: Successfully repaid 2,804.84 ETH (0.3% fee)
- Attacker net profit: ~$18.5M (via Cream protocol arbitrage)
- Confidence: 0.98
[PriceOracle Expert] Analysis result:
- Cream uses Uniswap V2 TWAP price oracle
- Attacker manipulated Uniswap pool in same block (add massive liquidity → borrow → remove liquidity)
- Caused crETH price undervaluation by 40%
- Confidence: 0.96
[AccessControl Expert] Analysis result:
- Cream contract lacks limits on single large borrows
- No delayed oracle update mechanism (vulnerable to same-block manipulation)
- Confidence: 0.92
[Orchestrator] Synthesis:
Root cause: Flash loan + Uniswap price manipulation + Cream oracle design flaw
Attack path:
1. Flash loan borrowed 2,804 ETH
2. Injected massive ETH into Uniswap, manipulating crETH/ETH price
3. Borrowed massive assets from Cream at undervalued price
4. Repaid flash loan, retained arbitrage profit
Remediation recommendations:
- Use Chainlink or other decentralized oracles
- Implement TWAP delayed updates (at least 3 blocks)
- Add single-borrow caps (liquidity % check)
Comparison with Manual Analysis :
Manual time: ~14 hours (requires deep understanding of Cream, Aave, Uniswap three-protocol interaction)
FaultSeeker time: 5.0 minutes
Conclusion consistency: 100% (root cause localization fully matches manual analysis report)
Case 2: Poly Network Cross-Chain Bridge Attack (August 2021)
Loss: $610 million (largest DeFi attack in history, later returned)
Attack Type: Access control vulnerability
Complexity: Cross-chain interaction (Ethereum + BSC + Polygon)
FaultSeeker Analysis Highlights :
[AccessControl Expert] Key finding:
- EthCrossChainManager contract has permission verification flaw
- putCurEpochConPubKeyBytes() function lacks caller permission check
- Attacker constructed malicious cross-chain messages, replaced verification public key
- Gained arbitrary cross-chain asset transfer permission
Root cause:
function putCurEpochConPubKeyBytes(bytes memory curEpochPkBytes) public {
// ❌ Missing onlyOwner or onlyKeeper modifier
ConKeepersPkBytes[..] = curEpochPkBytes;
}
Remediation recommendation:
+ function putCurEpochConPubKeyBytes(...) public onlyOwner {
ConKeepersPkBytes[..] = curEpochPkBytes;
}
Analysis time : 6.4 minutes (cross-chain interaction adds complexity)
Accuracy : Precisely located vulnerable function (consistent with post-incident audit report)
3.4 Boundary Case Analysis Scenarios Where FaultSeeker Excels
Multi-Stage Combination Attacks : Expert collaboration advantage evident (e.g., flash loan + price manipulation + reentrancy)Novel Attack Variants : LLM generalization stronger than rule enginesComplex Business Logic : Semantic understanding superior to pure syntax analysis Scenarios Where FaultSeeker Faces Limitations
Minimal Attacks : E.g., simple integer overflow (DAppFL actually faster and cheaper)Zero-Day Vulnerabilities : Entirely new attack patterns (unseen similar cases) may require multiple iterationsObfuscated Code : Deeply obfuscated contracts reduce LLM comprehension accuracy Insight : FaultSeeker isn't a silver bullet; best practice combines with static analysis tools (Slither/Mythril) and rule engines (DAppFL) for multi-layered defense.
Chapter 4: Practical Application Scenarios and Deployment Strategies 4.1 DeFi Protocol Incident Response Security incident occurs
↓
1. Real-time monitoring system (Forta/OpenZeppelin Defender) detects anomalous tx
↓
2. Automatically triggers FaultSeeker analysis
↓ (4-8 minutes)
3. Generates preliminary fault report
├─ Root cause localization
├─ Attack path reconstruction
├─ Affected scope assessment
└─ Remediation recommendations
↓
4. Security team validates report
↓
5. Execute emergency response
├─ Pause affected contracts
├─ Deploy remediation patches
└─ User notification
Real Case : A leading DEX's results after integrating FaultSeeker
Response Time : "Incident occurrence to contract pause" reduced from average 4 hours to 15 minutesLoss Control : An attack attempt identified and blocked after 2nd transaction, avoiding $2M potential lossFalse Positives : 0 false-alarm-triggered unnecessary pauses in 6 months 4.2 Security Audit Firm Efficiency Enhancement
Batch Analysis of Historical Cases
Input: All DeFi attacks from past 3 years (500+)
Output: Attack pattern knowledge base for improving audit checklists
Effect: Audit coverage increased 30% (discover more potential risk types)
Pre-Deployment Risk Assessment
Input: Contract code awaiting deployment + testnet transaction records
Output: Similarity analysis with known attack patterns
Effect: Early identification of high-risk designs (e.g., easily manipulated oracles)
Junior Analyst Empowerment
Scenario: Complex attack cases pre-screened by FaultSeeker
Process: AI analysis → junior analyst validation → senior expert review difficult points
Effect: Junior analyst handling capacity increased 50%, expert time saved 40%
An Audit Firm's Practice Data (anonymized):
Pre-integration: 20-person team, 12 audit projects/month
Post-integration: Same 20 people, 18 projects/month (+50%)
Customer satisfaction: Increased from 85% to 92% (more detailed reports, more comprehensive findings)
4.3 Investment Due Diligence
Project Security Rating
Input: Project contract code + existing security audit reports
Analysis: FaultSeeker simulates known attack patterns, assesses defense effectiveness
Output: Security score (A-F grade) + critical risk list
Competitive Comparison Analysis
Scenario: Investors choosing among multiple similar projects
Method: FaultSeeker horizontally compares security architectures of each project
Case: A VC used FaultSeeker to compare 5 lending protocols, found 2 had risk points similar to historical attacks, ultimately invested in the 3 with higher security
Investment scale: $5 million
FaultSeeker cost: $200 (40 projects × $5/project)
Value: Avoided investing in 2 high-risk projects, potential loss $2M (assuming 40% failure rate)
Actual ROI: 10,000x ($2M / $200)
4.4 Insurance Industry Applications DeFi Insurance Pain Points :
Slow manual claims review (average 72 hours)
Inaccurate risk pricing (lack of historical data analysis)
Difficult fraudulent claims identification (requires deep technical verification)
Claim submitted
↓
FaultSeeker rapid analysis (5-8 minutes)
├─ Verify attack authenticity
├─ Confirm loss amount
├─ Determine if within policy coverage
└─ Identify potential fraud (e.g., self-directed attacks)
↓
Claims decision recommendation → Final human approval
Dynamic Risk Pricing
Real-time analysis of protocol's historical security performance
Compare attack probability with similar protocols
Adjust premiums based on risk level (high-risk projects +50% premium, low-risk -20%)
A DeFi Insurance Platform's Data :
Claims processing time: Reduced from 72 hours to 6 hours (including human approval)
Fraud identification: Identified 3 self-directed attacks in 6 months (saved $800K payouts)
Premium optimization: Lower premiums for low-risk projects, attracting more quality projects to insure
4.5 Regulatory Compliance Support Regulatory Authority Needs :
Standardized security incident reports
Traceable evidence chains
Cross-project risk comparison
Unified Report Format
All security incidents generate analysis reports with same structure
Facilitates regulatory database construction and trend analysis
Evidence Chain Integrity
Complete reasoning process from on-chain data → code analysis → root cause localization
Meets legal litigation and compliance review requirements
Industry Risk Map
Batch-analyze all DeFi protocols, generate systemic risk assessments
Support regulatory policymaking (e.g., "high-risk protocols require additional audits")
Chapter 5: Technical Challenges and Future Evolution 5.1 Current Technical Limitations Limitation 1: LLM Hallucination Risk While multi-expert cross-validation reduces hallucination probability, risks remain:
False Discoveries : LLM may fabricate non-existent vulnerabilities (especially in highly complex code)Missing Real Threats : May lack recognition capability for entirely new attack patterns # Multi-layer verification mechanism
class VerificationPipeline:
def validate_finding(self, llm_result):
# 1. Static analysis tool cross-validation
slither_check = run_slither(llm_result.contract)
if not slither_check.confirms(llm_result.vulnerability):
llm_result.confidence *= 0.7 # Reduce confidence
# 2. Symbolic execution verification
if llm_result.type == "reentrancy":
mythril_result = run_mythril(llm_result.contract)
if mythril_result.confirms_reentrancy():
llm_result.confidence *= 1.2 # Increase confidence
# 3. Human review threshold
if llm_result.confidence < 0.8:
return "NEEDS_HUMAN_REVIEW"
elif llm_result.confidence > 0.95:
return "HIGH_CONFIDENCE_AUTO_APPROVE"
else:
return "MEDIUM_CONFIDENCE"
Limitation 2: Novel Attack Generalization FaultSeeker performs excellently on 115 known cases, but for entirely new attack patterns:
Recognition Delay : First encounters may misjudge or missAdaptive Learning : Requires continuous knowledge base updates
Continuous Learning Mechanism
Weekly extraction of 1-3 cases from latest security incidents to update expert knowledge
Similar to ETAAcademy's "rolling update" model
Zero-Shot Reasoning Enhancement
Improve prompt design, boost reasoning capability for "unseen" attacks
Example: "Even if you haven't seen this attack, analyze based on Solidity security principles..."
Community Contribution Mechanism
Open interfaces for security researchers to submit new attack patterns
Build CVE-like DeFi vulnerability knowledge base
Limitation 3: Cross-Chain Analysis Complexity Poly Network case shows cross-chain attack analysis takes longer (6.4 minutes vs 5-minute average) because:
Need to understand multi-chain VM differences (EVM vs WASM)
Cross-chain message validation logic more complex
Fund tracking spans multiple blockchains
2025 Q4 : Improve Ethereum L2 support (Arbitrum, Optimism, Base)2026 Q1 : Extend to Solana, Aptos and other non-EVM chains2026 Q2 : Cross-chain bridge-specific analysis module (focus on LayerZero, Wormhole and other cross-chain protocols) Direction 1: Proactive Defense Capability Current: Post-incident forensics (analysis after attacks occur)
Future: Preventive detection (pre-deployment scanning + real-time interception)
┌─────────────────────────────────────┐
│ FaultSeeker 2.0 Architecture Vision │
├─────────────────────────────────────┤
│ │
│ 1. Development Phase │
│ ├─ IDE plugins (Remix/VSCode) │
│ ├─ Real-time security tips │
│ └─ Historical case similarity │
│ │
│ 2. Pre-Deployment Review │
│ ├─ CI/CD integration │
│ ├─ Automated security scoring │
│ └─ Auto-reject high-risk patterns│
│ │
│ 3. Runtime Monitoring │
│ ├─ Mempool tx pre-analysis │
│ ├─ Real-time suspicious tx │
│ │ interception (<1 sec) │
│ └─ Firewall contract linkage │
│ │
│ 4. Post-Incident Forensics │
│ └─ 4-8 minute deep analysis │
│ │
└─────────────────────────────────────┘
Latency Optimization : Runtime detection needs compression from 4-8 minutes to <1 second
Approach: Pre-trained "fast mode" (sacrifice depth for speed) + "deep mode" (existing architecture)
Similar to antivirus "real-time protection" (heuristic) + "full scan" (deep) dual modes
False Positive Control : Real-time interception requires extremely low false positives (<0.1%, otherwise affects normal transactions)
Approach: Conservative strategy, only intercept high-confidence threats (>0.99)
Suspicious transactions flagged but not intercepted, left to user decision
Direction 2: Knowledge Graph Enhancement Build "DeFi Attack Knowledge Graph" (DAKG):
┌─────────────────────────────────────────┐
│ DeFi Attack Knowledge Graph (DAKG) │
├─────────────────────────────────────────┤
│ │
│ Entity Types: │
│ • Attacker addresses │
│ • Victim protocols │
│ • Vulnerability types │
│ • Attack techniques │
│ • Remediation patterns │
│ │
│ Relationship Types: │
│ • Attacker -[executes]-> Attack event │
│ • Attack event -[exploits]-> Vuln type │
│ • Vuln type -[maps to]-> Fix pattern │
│ • Attack event -[evolved from]-> Historical│
│ │
│ Applications: │
│ • Attacker profiling (identify repeat offenders)│
│ • Attack pattern evolution analysis │
│ • Auto-generate defense strategies │
│ • Predict future attack trends │
│ │
└─────────────────────────────────────────┘
Phase 1 (2025 Q4): Build initial graph from 115 casesPhase 2 (2026 Q1): Expand to 1000+ historical casesPhase 3 (2026 Q2): Real-time updates (daily new incidents auto-archived)Phase 4 (2026 Q3): Graph neural network analysis, predict attack correlations Direction 3: Federated Learning & Privacy Protection Challenge : Private protocols unwilling to upload code to cloud FaultSeeker
Solution : Federated learning architecture
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Project A │ │ Project B │ │ Project C │
│ (On-premise) │ │ (On-premise) │ │ (On-premise) │
│ ┌────────┐ │ │ ┌────────┐ │ │ ┌────────┐ │
│ │Local │ │ │ │Local │ │ │ │Local │ │
│ │Model │ │ │ │Model │ │ │ │Model │ │
│ └────────┘ │ │ └────────┘ │ │ └────────┘ │
│ ↓ │ │ ↓ │ │ ↓ │
│ ┌────────┐ │ │ ┌────────┐ │ │ ┌────────┐ │
│ │Gradient│ │ │ │Gradient│ │ │ │Gradient│ │
│ │Encrypt │ │ │ │Encrypt │ │ │ │Encrypt │ │
│ └────────┘ │ │ └────────┘ │ │ └────────┘ │
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
│ │ │
└──────────────────┼──────────────────┘
↓
┌─────────────────┐
│ Central │
│ Aggregation │
│ Server │
│ (Gradients only)│
│ ┌───────────┐ │
│ │Global Model│ │
│ └───────────┘ │
└─────────────────┘
✅ Code stays on-premise, protects trade secrets
✅ Shared learning outcomes, all participants benefit
✅ Differential privacy guarantee, cannot reverse-engineer original data
FaultSeeker (heuristic) + Certora/K Framework (mathematical proof) = Ultimate security assurance
Latest Technical Progress (June 2025) :
Certora announced support for Rust smart contract formal verification, expanding to Solana SBF (Solana Bytecode Format) ecosystem:
Technical Breakthrough : Certora Prover now verifies Rust-written on-chain programsCoverage : Expands from EVM (Solidity) to Solana, Aptos, Sui and other Rust-based contractsVerification Capabilities : Supports formal verification of Rust features like Ownership model and Borrow Checker K Framework Integration Path :
K Framework provides formal semantics for Ethereum Virtual Machine (KEVM), enabling deep integration with FaultSeeker:
┌────────────────────────────────────────────────────────┐
│ FaultSeeker + Certora + K Framework Integration │
├────────────────────────────────────────────────────────┤
│ │
│ Layer 1: FaultSeeker Heuristic Analysis │
│ ┌──────────────────────────────────────────┐ │
│ │ • Rapid scan (4-8 min) │ │
│ │ • Generate vulnerability hypotheses │ │
│ │ • Confidence scoring (0-1) │ │
│ └──────────────────────────────────────────┘ │
│ ↓ │
│ Layer 2: K Framework Static Verification (Mid-tier) │
│ ┌──────────────────────────────────────────┐ │
│ │ • KEVM semantic analysis (5-10 min) │ │
│ │ • Symbolic execution verification │ │
│ │ • State space exploration │ │
│ └──────────────────────────────────────────┘ │
│ ↓ │
│ Layer 3: Certora Formal Proof (Final tier) │
│ ┌──────────────────────────────────────────┐ │
│ │ • SMT solver verification (30-60 min) │ │
│ │ • Mathematical proof generation │ │
│ │ • Counterexample construction │ │
│ └──────────────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────┘
1. FaultSeeker rapid scan (4-8 minutes)
↓
2. Suspected vulnerabilities found (confidence > 0.7) → trigger K Framework
↓
3. K Framework KEVM analysis (5-10 minutes)
├─ Confirms suspicion → proceed to Certora deep verification
├─ Rules out risk → mark FaultSeeker false positive
└─ Uncertain → manual review
↓
4. Certora formal proof (30-60 minutes)
├─ Proves vulnerability exists → high-priority remediation + generate PoC
├─ Proves vulnerability doesn't exist → update FaultSeeker model
└─ Cannot prove → mark as complex edge case
Multi-Chain Support Strategy (2025-2026 Roadmap):
EVM Chains : Certora Prover + K Framework KEVM (mature)Solana : Certora Rust verification + Anchor framework integration (2025 Q4)Move Ecosystem (Aptos/Sui): Move Prover + Certora extension (2026 Q1)Cross-Chain Bridges : LayerZero/Wormhole dedicated formal specifications (2026 Q2) Real-World Application Case :
A DeFi protocol integrating FaultSeeker + Certora workflow:
Discovery Phase : FaultSeeker found 3 suspected vulnerabilities in pre-deployment review (15 minutes)Verification Phase : Certora proved 2 as real vulnerabilities, 1 false positive (2 hours)Remediation Effect : Avoided potential $5M+ loss, audit efficiency improved 60% Cost Savings : Traditional manual deep audit requires 8 hours, automated process only 2.25 hours
FaultSeeker provides "where problems might be" hypotheses (high recall)
K Framework provides "quick preliminary verification" (filters obvious false positives)
Certora provides "whether problems definitely exist" rigorous proof (high precision)
Three-tier architecture: Speed (FaultSeeker) + Efficiency (K) + Determinism (Certora)
5.3 Ethical and Social Impact FaultSeeker can locate vulnerabilities, equally usable by attackers:
White hats : Rapidly fix exploited vulnerabilitiesBlack hats : Discover unexploited vulnerabilities for attacks Responsible Use Principles :
Access Control
Only authorize KYC-verified security teams
Audit firms, exchanges and other legitimate institutions
Log all analysis activities (traceable)
Delayed Disclosure
After finding new vulnerabilities, notify project for remediation first (typically 90-day window)
Similar to Google Project Zero's responsible disclosure
Community Oversight
Establish ethics committee to review FaultSeeker use cases
Regular transparency reports (analyzed cases, vulnerabilities found, fix rate)
Security analyst roles transform from "manual analysis" to "AI supervision"
Junior position demand decreases, but senior expert demand increases (validate AI results)
New careers emerge: "AI Security Engineers" (tune FaultSeeker and similar tools)
Analyst skill upgrades: from technical analysis to strategic decision-making, defense design
Analogy : Automated testing didn't eliminate QA, but shifted QA from "manual clicking" to "test strategy design"
Positive : Lowers security barriers, small projects can afford professional-grade analysis
Negative : May exacerbate Matthew effect (large institutions adopt earlier, widening gaps)
Provide free/low-cost tiers for open-source projects and small teams
Education programs: free training for security personnel on using FaultSeeker
Public goods funding: obtain community funding through Gitcoin and other platforms, reducing service costs
Chapter 6: Industry Ecosystem and Competitive Landscape 6.1 Target Market Segmentation | Market Segment | Market Size | Willingness to Pay | Adoption Priority |
|----------------|-------------|-------------------|------------------|
| DeFi Protocols | TVL $100B+ | Very High | Mature protocols > Emerging projects |
| Security Audit Firms | Annual revenue $500M+ | High | Leading institutions first |
| Exchanges | Compliance-driven | Medium | Tier-1 exchanges |
| Web3 Infrastructure | Ecosystem value-add | Medium | Alchemy/Infura types |
| Insurance Platforms | Cost reduction/efficiency | High | Nexus Mutual types |
Growth Potential Analysis :
DeFi protocols: Each 1% adoption rate ~$10M annual market (assuming $100K/protocol annual fee)
Audit firms: Top 20 annual procurement budget ~$50M (efficiency tools)
Total Addressable Market (TAM): Conservative estimate $200M+/year
DAppFL (UC Berkeley open-source project)
Strengths: Open-source, fast, deterministic
Weaknesses: Rule engine limitations, cannot handle complex logic
Competitive strategy: FaultSeeker targets high-end market (complex cases), DAppFL suitable for initial screening
Forta Network (Real-time monitoring)
Strengths: Decentralized, real-time detection
Weaknesses: Focuses on detection rather than root cause analysis
Competitive strategy: Complementary integration (Forta detects + FaultSeeker analyzes)
OpenAI Code Analyzer (hypothetical)
Threat: General-purpose LLM giants may launch competing products
Response: FaultSeeker's domain specialization advantage (blockchain knowledge depth), multi-agent architecture moat
Traditional Security Vendors (CertiK, Quantstamp)
Threat: Integrate FaultSeeker-like technology into existing services
Response: First-mover advantage, technical patents, open API attracting ecosystem
Model 1: SaaS Subscription (Mainstream) Pricing tiers:
- Startup: $99/month, 10 analyses/month
- Growth: $499/month, 50 analyses/month, priority support
- Enterprise: $2,999/month, unlimited analyses, dedicated deployment
- Single analysis: $10/use
- Bulk pack: $500/100 uses (50% discount)
- API calls: $0.10/1K tokens (ChatGPT-like pricing)
Model 3: Private Deployment - Annual license: $50K-$200K
- Includes: On-premise model, technical support, custom development
- Target customers: Major exchanges, regulatory agencies
- DeFi attack database access: $1K/month
- Knowledge graph API: $5K/month
- Custom reports: $10K/report
Year 1: SaaS-led (rapid customer acquisition)
Year 2: Private deployment growth (large customers)
Year 3: Data services become high-margin business
6.4 Go-to-Market Strategy Stage 1: Early Adopters (0-6 months) Target : 10-20 paying customers
Strategy :
Free trial (100 analyses first month)
Key customers: Leading DeFi protocols (Uniswap, Aave, etc.)
KOL marketing: Speaking at Devcon, ETHDenver and other conferences
Case studies: Publicly analyze classic cases like Cream Finance
Stage 2: Scaling (6-18 months) Target : 200+ paying customers
Strategy :
Partnerships: Strategic collaboration with OpenZeppelin, ConsenSys
Product matrix: Launch IDE plugins, CI/CD integrations
Geographic expansion: North America → Europe → Asia
Community building: Open-source some components, build developer community
Stage 3: Ecosystem Dominance (18-36 months) Target : Become industry standard
Strategy :
API opening: Let third-party developers build applications on FaultSeeker
Certification program: "FaultSeeker Certified" becomes protocol security label
M&A integration: Acquire complementary tools (e.g., static analysis, formal verification)
IPO/Acquisition: Seek exit opportunities or independent development
Chapter 7: Academic Contributions and Future Research Directions 7.1 Theoretical Contributions Contribution 1: Cognitive Architecture Application in Software Security FaultSeeker proves the effectiveness of applying cognitive science principles (memory, attention) to AI system design:
Human Expert Analysis Process FaultSeeker Architecture Mapping
──────────────────────────── → ──────────────────────────
1. Attention: Global scan, focal points → Stage 1: Tx-level forensics
2. Working Memory: Load relevant info → RAG + context management
3. Long-term Memory: Invoke expertise → Expert agents (pre-trained knowledge)
4. Reasoning: Synthesize info, conclude → Orchestrator coordination & synthesis
Provides paradigm for "how to design LLM systems for complex tasks"
Transferable to other domains (medical diagnosis, legal analysis, financial auditing)
Promotes interdisciplinary fusion of AI and cognitive science
Contribution 2: Multi-Agent Collaboration Paradigm Validation
Multi-expert collaboration outperforms single giant model in security analysis (91% vs 82% accuracy)
Expert division enhances depth, orchestrator enhances consistency
Proves "small specialized model combinations > single general-purpose large model" hypothesis (for specific tasks)
Future LLM applications may not be "bigger is better" but "precise division of labor"
Orchestrator design is key (how to assign tasks, synthesize conclusions)
Provides practical validation for AutoGen, LangChain and other frameworks
Contribution 3: Domain-Specialized LLM Methodology FaultSeeker demonstrates how to adapt general-purpose LLMs to professional domains:
Knowledge Injection : Embed blockchain security knowledge through prompt engineeringTask Decomposition : Break complex analysis into manageable sub-tasksValidation Mechanisms : Combine with deterministic tools (static analysis) to reduce hallucinationsContinuous Learning : Update knowledge base from new cases
Medical AI: Multi-expert (pathology, imaging, genetics) collaborative diagnosis
Legal AI: Contract review experts + case law analysis experts
Financial AI: Credit assessment + fraud detection + compliance review
7.2 Future Research Directions Direction 1: Self-Supervised Learning and Model Distillation Problem : Relying on LLM APIs is costly and dependent on vendors
Use FaultSeeker to generate large volumes of analysis reports as training data
Distill into specialized smaller models (7B-13B parameters)
Target: Cost reduced to current 1/10, speed increased 3-5x
How to ensure distilled models don't lose critical reasoning capabilities
How to handle long contexts (blockchain analysis requires large context windows)
Direction 2: Explainable AI and Trust Mechanisms Problem : How do users trust FaultSeeker's conclusions?
Generate "analysis process visualization" (mind map-like)
Provide "confidence decomposition" (which evidence supports, which contradicts)
Introduce "refutation mechanism" (AI proactively seeks counterexamples, enhancing robustness)
Conclusion: Flash loan attack (Confidence: 0.92)
Evidence chain:
✅ [0.98] Aave flash loan interface call confirmed (FlashLoanExpert)
✅ [0.96] Uniswap 40% abnormal price fluctuation (PriceOracleExpert)
⚠️ [0.65] No obvious reentrancy pattern found (ReentrancyExpert)
✅ [0.92] Single-block borrow-manipulate-repay completion (LogicFlawExpert)
Synthesis logic:
1. Flash loan confirmed + price manipulation → combo attack hypothesis
2. No reentrancy pattern → rule out reentrancy attack
3. Single-block execution → matches flash loan typical signature
→ Conclusion: Flash loan + price manipulation combo attack
Refutation test:
? Could this be normal arbitrage rather than attack?
→ Check profit source: Victim protocol lost $18.5M, inconsistent with normal arbitrage
→ Determination: Malicious attack
Direction 3: Adversarial Testing and Robustness Problem : Can attackers "fool" FaultSeeker?
Build "adversarial attack dataset" (intentionally design hard-to-detect attacks)
Red team testing: Security researchers attempt to bypass FaultSeeker detection
Continuous adversarial training: Strengthen model with failure cases
Code obfuscation attacks: Can FaultSeeker identify deeply obfuscated malicious contracts?
Time-dispersed attacks: Attack decomposed into dozens of seemingly normal transactions
Legitimization disguise: Attack code disguised as legitimate governance proposals
Direction 4: Cross-Chain Security Analysis Theory Problem : Lack of unified theoretical framework for cross-chain interaction security analysis
Formalize cross-chain security models (state sync, message verification)
Cross-chain attack taxonomy (bridge contract vulnerabilities, consensus inconsistencies, replay attacks, etc.)
Automated cross-chain security proofs
Publish top-tier conference papers (NDSS, IEEE S&P, USENIX Security)
Promote cross-chain security standards (collaborate with Web3 Foundation, EF, etc.)
7.3 ASE 2025 Conference Significance
Full Name : 40th IEEE/ACM International Conference on Automated Software EngineeringRanking : CORE A* (top software engineering conference)Time : November 16-20, 2025Location : Seoul, South KoreaFormat : Poster-first presentation (emphasizes interactive discussion) Significance of FaultSeeker Acceptance :
Academic Recognition : Top conference acceptance proves technological innovation and academic valueCommunity Exposure : Connect with global software engineering researchers and practitionersCollaboration Opportunities : Establish partnerships with universities, research institutions (e.g., joint PhD programs)Talent Attraction : Top conference publications make recruiting top researchers easier
Inspire follow-up research (expect 10+ papers citing FaultSeeker within 6-12 months)
Become benchmark case for LLM security applications
Drive ASE community focus on blockchain security (cross-domain fusion)
FaultSeeker, as an LLM-driven blockchain fault localization framework, demonstrates outstanding performance on 115 real malicious transaction cases through its cognitive science-inspired two-stage architecture and multi-agent collaboration mechanism: compressing analysis time from 16.7 hours to 4.4-8.6 minutes, reducing costs by 99.7%, and achieving 91% accuracy, surpassing existing solutions including DAppFL, GPT-4o, Claude 3.7 Sonnet, and DeepSeek R1.
Efficiency Revolution : 115-227x speedup enables near real-time security responseCost Optimization : $1.55-$4.53/transaction democratizes professional-grade analysisTechnical Innovation : Cognitive architecture + multi-agent collaboration provides paradigm for complex-task AI system designEcosystem Empowerment : Benefits multiple stakeholders from DeFi protocols, audit firms to investors and regulators
DeFi Protocols : Integrating FaultSeeker significantly improves incident response speed, reducing loss risksSecurity Audit Firms : As efficiency tool, processing capacity increased 30-50%Investors : Due diligence tool quantifying project security risksInsurance Industry : Automated claims review, dynamic risk pricingRegulatory Agencies : Standardized reports, systemic risk monitoring Future Outlook :
FaultSeeker's success validates LLM potential in professional security domains. With technological evolution (proactive defense, knowledge graphs, federated learning, formal verification integration), FaultSeeker aspires to evolve from "post-incident forensics tool" to "full lifecycle security guardian," driving systemic security improvements in the Web3 ecosystem.
For blockchain security practitioners, FaultSeeker represents not just a tool but the beginning of a new paradigm: human experts collaborating with AI, finding optimal balance among speed, cost, and accuracy, jointly safeguarding the decentralized future worth hundreds of billions of dollars.
About the ASE 2025 Paper : This analysis is based on the ASE 2025 conference paper "FaultSeeker: LLM-Empowered Framework for Blockchain Transaction Fault Localization," to be presented at the conference in Seoul, South Korea, November 16-20, 2025.
About the Authors : The Innora Technology Team focuses on AI-driven cybersecurity technology research, committed to promoting cutting-edge academic achievements in practical security defense applications.
Copyright Notice : This article is licensed under CC BY-SA 4.0. Please attribute when sharing.
Document Version : 1.0
Last Updated : October 6, 2025
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