analyzing-ethereum-smart-contract-vulnerabilities skill
Perform static and symbolic analysis of Solidity smart contracts using
Is the analyzing-ethereum-smart-contract-vulnerabilities skill safe?
Clean: nothing in its files matched our rules. We read 4 files in the folder on 2026-09-28.
No findings.
Install the analyzing-ethereum-smart-contract-vulnerabilities skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills mkdir -p ~/.claude/skills cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-ethereum-smart-contract-vulnerabilities ~/.claude/skills/analyzing-ethereum-smart-contract-vulnerabilities
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
Analyzing Ethereum Smart Contract Vulnerabilities
Overview
Smart contract vulnerabilities have led to billions of dollars in losses across DeFi protocols. Unlike traditional software, deployed smart contracts are immutable and handle real financial assets, making pre-deployment security analysis critical. Slither performs fast static analysis using an intermediate representation to detect over 90 vulnerability patterns in seconds, while Mythril uses symbolic execution and SMT solving to discover complex execution path vulnerabilities like reentrancy and integer overflows. This skill covers running both tools against Solidity contracts, interpreting results, triaging findings by severity, and generating audit reports.
When to Use
- When investigating security incidents that require analyzing ethereum smart contract vulnerabilities
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Python 3.10+ with pip
- Slither (pip install slither-analyzer) and solc compiler
- Mythril (pip install mythril) with solc-select for compiler version management
- Solidity source code or compiled contract bytecode
- Foundry or Hardhat development framework (optional, for project-level analysis)
Steps
Step 1: Run Slither Static Analysis
Execute Slither against the contract codebase to identify vulnerability patterns, optimization opportunities, and code quality issues using its 90+ built-in detectors.
Step 2: Run Mythril Symbolic Execution
Run Mythril deep analysis to explore execution paths and discover reentrancy, unchecked external calls, and arithmetic vulnerabilities that require path-sensitive analysis.
Step 3: Triage and Correlate Findings
Combine results from both tools, deduplicate findings, assess severity based on exploitability and financial impact, and filter false positives.
Step 4: Generate Audit Report
Produce a structured audit report with vulnerability descriptions, affected code locations, exploit scenarios, and remediation recommendations.
Expected Output
JSON report listing vulnerabilities with SWC (Smart Contract Weakness Classification) identifiers, severity ratings, affected functions, and suggested fixes.
More skills from mukul975/Anthropic-Cybersecurity-Skills
- Aabusing-dpapi-for-credential-accessExtract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use during authorized red-team credential-access engagements after gaining a foothold or when triaging DPAPI blobs pulled from a host.
- Aabusing-shadow-credentials-for-privescTake over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target's NT hash without a password reset. Use when BloodHound shows GenericWrite/GenericAll/AddKeyCredentialLink over a target, as a stealthier alternative to ForceChangePassword, during authorized red-team engagements.
- Aachieving-cmmc-level-2-compliancePrepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
- Aacquiring-disk-image-with-dd-and-dcflddCreate forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.
- Aanalyzing-active-directory-acl-abuseDetect dangerous ACL misconfigurations in Active Directory using ldap3
- Aanalyzing-android-malware-with-apktoolPerform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.
- Danalyzing-api-gateway-access-logs'Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect
- Aanalyzing-apt-group-with-mitre-navigatorQuery ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.
- Aanalyzing-azure-activity-logs-for-threats'Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query
- Aanalyzing-bootkit-and-rootkit-samples'Analyzes bootkit and advanced rootkit malware infecting the Master
- Aanalyzing-browser-forensics-with-hindsightParse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user's web activity from a browser profile.
- Aanalyzing-campaign-attribution-evidenceSystematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.