From 7bc1b2be1c5a5bbd60c149168a88d3c29364ddfa Mon Sep 17 00:00:00 2001 From: mukul975 Date: Thu, 19 Mar 2026 19:47:28 +0100 Subject: [PATCH] Remove audit report from repo --- .gitignore | 1 + AUDIT_REPORT.md | 398 ------------------------------------------------ 2 files changed, 1 insertion(+), 398 deletions(-) delete mode 100644 AUDIT_REPORT.md diff --git a/.gitignore b/.gitignore index a066636b..e26138f5 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,4 @@ Thumbs.db *.swp launch/ extract_attack.py +AUDIT_REPORT.md diff --git a/AUDIT_REPORT.md b/AUDIT_REPORT.md deleted file mode 100644 index cd90f546..00000000 --- a/AUDIT_REPORT.md +++ /dev/null @@ -1,398 +0,0 @@ -# Cybersecurity Skills Repository -- Security & Quality Audit Report - -**Audit Date:** 2026-03-17 -**Repository:** Anthropic-Cybersecurity-Skills -**Auditors:** 15-agent automated audit team (silly-herding-tide) -**Scope:** All 742 skill directories, 734 SKILL.md files, 733 agent.py files - ---- - -## Executive Summary - -A comprehensive 14-task automated audit of 742 cybersecurity skill directories (734 with SKILL.md, 733 with agent.py) found **zero critical security vulnerabilities** (no eval/exec on live data, no prompt injection, no YAML injection, no real hardcoded secrets) but identified **25 HIGH-severity shell injection patterns** using `subprocess.run(shell=True)` with f-string interpolation, **178 instances of disabled SSL verification**, and **33 HTTP requests missing timeouts**. The repository content is verified as high-quality (87% of sampled skills confirmed real against official documentation, 0% fake), but has systemic quality issues: all 734 SKILL.md files contain extra frontmatter fields beyond the standard spec, 697/734 use an alternate body template lacking `## Instructions`/`## Examples` sections, and 9 offensive tools lack disclaimers in both their SKILL.md and agent.py files. The repo is **educational-grade, not production-safe** -- it is well-researched reference material with real code, but should not be deployed as-is in any environment accepting untrusted input. - ---- - -## Security Findings - -### CRITICAL - -**eval/exec/pickle/marshal on live data: 0 findings** -- Scanned all 733 agent.py files for `eval(`, `exec(`, `pickle.loads(`, `marshal.loads(` used on live data -- 16 `eval(` matches were all string literals (SPL query syntax, regex patterns, CSP header text) -- 9 `exec(` matches were all function/variable names (e.g., `detect_psexec`) or regex patterns -- Zero instances of `pickle.loads()` or `marshal.loads()` -- **Verdict: CLEAN** - -**Prompt injection in SKILL.md: 0 exploitable findings** -- Scanned all 734 SKILL.md files for "ignore previous", "you are now", "ADMIN:", ``, ``, `[INST]`, "as a helpful AI", hidden HTML comments, zero-width characters, base64 payloads -- 21 files matched patterns, but all are educational content explaining prompt injection as a security topic (e.g., skills about detecting/preventing prompt injection) -- **Verdict: CLEAN** (educational context, not weaponized) - -**YAML injection in frontmatter: 0 findings** -- Scanned all 734 SKILL.md frontmatter blocks for injection patterns -- All matches were in body content (educational examples), not in frontmatter -- **Verdict: CLEAN** - -**Real hardcoded secrets (API keys, tokens): 0 findings** -- Scanned for AKIA*, sk-*, ghp_*, real tokens, embedded base64 blobs -- Found default/example credentials only (see MEDIUM section) -- **Verdict: CLEAN** - -### HIGH - -**1. Shell injection via subprocess.run(shell=True) -- 25 instances** - -25 agent.py files use `subprocess.run(cmd, shell=True, ...)`, and at least 4 use f-string interpolation of file paths directly into shell commands (e.g., `f"strings -n {min_length} {filepath}"`). If any of these scripts ever received untrusted input, shell injection would be trivial. - -Top-risk files (f-string + shell=True): -- `analyzing-linux-elf-malware/scripts/agent.py` (lines 88, 129, 138, 151) -- **HIGHEST RISK: compound vulnerability.** Uses raw `sys.argv[1]` (not even argparse), flows unsanitized into both `open(filepath, "rb")` (path traversal at lines 25, 46, 67, 122) AND 4 `shell=True` f-string subprocess calls (shell injection). A malicious filename could both traverse the filesystem and execute arbitrary commands. -- `analyzing-network-traffic-for-incidents/scripts/agent.py` (lines 22, 35, 61, 124, 138) -- `performing-threat-emulation-with-atomic-red-team/scripts/agent.py` (lines 99, 128) -- `performing-privilege-escalation-assessment/scripts/agent.py` (line 30) - -**Mitigating factor:** All scripts are CLI tools invoked locally via argparse (or sys.argv), not web-exposed. The user already has shell access. - -**Risk: HIGH in reuse/integration contexts, LOW for current local-CLI usage.** - -**2. Dynamic imports via __import__() -- 8 instances** - -8 agent.py files use `__import__()` for inline imports of standard library modules (datetime, time, collections, os). Not malicious, but obscures dependencies and is an anti-pattern. - -Files: `analyzing-threat-intelligence-feeds`, `bypassing-authentication-with-forced-browsing`, `conducting-api-security-testing`, `conducting-man-in-the-middle-attack-simulation`, `exploiting-ipv6-vulnerabilities`, `implementing-zero-trust-with-hashicorp-boundary`, `performing-hash-cracking-with-hashcat`, `performing-security-headers-audit` - -**Risk: MEDIUM (poor practice, not exploitable)** - -**3. Missing authorized-testing disclaimers -- 9 CRITICAL skills** - -9 offensive security skills have NO disclaimer in EITHER their SKILL.md or agent.py: -1. `exploiting-excessive-data-exposure-in-api` -2. `performing-graphql-depth-limit-attack` -3. `performing-graphql-introspection-attack` -4. `performing-http-parameter-pollution-attack` -5. `performing-jwt-none-algorithm-attack` -6. `performing-supply-chain-attack-simulation` -7. `performing-web-cache-deception-attack` -8. `conducting-internal-network-penetration-test` -9. `conducting-mobile-application-penetration-test` - -An additional 7 skills are missing disclaimers in agent.py only, and 20 are missing disclaimers in SKILL.md only. Total: 36 of 58 offensive skills have at least one missing disclaimer. - -**Risk: HIGH (legal/liability concern for offensive tooling)** - -### MEDIUM - -**1. Disabled SSL verification (verify=False) -- 178 instances** -- 178 occurrences across agent.py files explicitly disable SSL certificate verification -- Common in tools connecting to local/lab instances (Splunk, SIEM, Nessus), but unsafe if pointed at production endpoints -- **Risk: MEDIUM** - -**2. HTTP requests without timeout -- 33 instances** -- 33 HTTP request calls across agent.py files lack a `timeout` parameter -- Can cause indefinite hangs if target is unresponsive -- **Risk: MEDIUM** - -**3. HTTP URLs instead of HTTPS -- 76 agent.py files** -- 76 scripts reference `http://` URLs -- Some are intentional (testing HTTP-specific vulnerabilities), others are careless defaults -- **Risk: LOW-MEDIUM** - -**4. Default/example credentials in code -- ~9 instances** -- `neo4j`/`bloodhound` (BloodHound tool default) -- `admin`/`admin` (GVM default) -- `kismet`/`kismet` (Kismet default) -- `Harbor12345` (Harbor default) -- `SecureP@ss123` (demo password) -- All are well-known tool defaults or demo values, not real secrets -- **Risk: LOW (tool defaults, not real credentials)** - -**5. Path traversal -- systemic but low-exploitability** -- ~342 agent.py files use `open()` with `args.*` parameters without path sanitization -- ~43 scripts create directories from unsanitized user input (`os.makedirs(args.output_dir)`) -- 1 script uses `shutil.rmtree()` on a derived path (`implementing-immutable-backup-with-restic`) -- Zero scripts validate that resolved paths stay within an expected base directory -- **Risk: LOW for CLI tools (user already has filesystem access), HIGH if ever web-exposed** - -### LOW - -**1. SQL injection patterns -- 6 MEDIUM findings** -- 6 agent.py files use SQL patterns that could be vulnerable (string formatting in queries) -- Limited scope -- most are local SQLite usage in forensics/logging contexts -- **Risk: MEDIUM (localized)** - -**2. Minor format issues** (see Quality Findings below) - ---- - -## Quality Findings - -### SKILL.md Frontmatter Compliance (Task #4 -- auditor-4) - -**732 of 734 SKILL.md files (99.7%) contain 6 extra frontmatter fields** beyond the minimal `name` + `description` spec: -- Extra fields present in nearly all files: `domain`, `subdomain`, `tags`, `version`, `author`, `license` -- **2 files have YAML parse errors** (unescaped colons in values) -- **ALL `name` values pass validation:** lowercase-with-hyphens, max 64 chars, no "claude" or "anthropic" -- **ALL `description` values pass validation:** under 1024 characters -- **Compliance with minimal two-field spec: 0%** (all have extra fields) -- **Compliance with extended format: 732/734 (99.7%)** (2 YAML errors) - -**Verdict:** The frontmatter is internally consistent but uses a richer schema than the minimal two-field standard. This is a format standardization finding (the cybersecurity repo uses a different template than the ai-agents repo), not a security vulnerability. The 2 YAML parse errors should be fixed. - -### SKILL.md Body Structure - -Two distinct templates are in use across the repository: - -**Primary template (697/734 = 95%):** Uses sections like `## When to Use`, `## Key Concepts`, `## Prerequisites`, `## Workflow`, `## Tools & Systems`, `## Output Format`, `## Common Scenarios`. Does NOT include `## Instructions` or `## Examples`. - -**Standard template (37/734 = 5%):** Uses `## Instructions` and `## Examples` sections per the original spec. - -Section presence across all 734 files: -- `## Prerequisites`: 627 (85%) -- `## Key Concepts`: 438 (60%) -- `## Workflow`: 369 (50%) -- `## When to Use`: 369 (50%) -- `## Tools & Systems`: 350 (48%) -- `## Overview`: 318 (43%) -- `## Output Format`: 326 (44%) -- `## Common Scenarios`: 300 (41%) -- `## Instructions`: 37 (5%) -- `## Examples`: 37 (5%) - -**Quality issues:** -- Stub/minimal SKILL.md files (under 20 lines): **10 files** -- Placeholder text (`TODO`, `FIXME`, `lorem ipsum`, `placeholder`): **0 files** (per auditor-5 deep scan) -- Average SKILL.md length: **218 lines** (substantial content) - -### agent.py Quality - -- Total agent.py files: **733** -- Average length: **178 lines** (non-trivial implementations) -- Files under 10 lines: **0** (none suspiciously short) -- Total lines of Python code: **130,466** -- Boilerplate/generic agent.py detected: **~4 out of 30 sampled** (13%) -- these use a generic HTTP-request template instead of tool-specific implementation - -### Missing Files - -- Directories missing SKILL.md: **8** (all ransomware/recovery-related batch additions) - - `analyzing-ransomware-payment-wallets` - - `building-ransomware-playbook-with-cisa-framework` - - `deploying-decoy-files-for-ransomware-detection` - - `detecting-ransomware-encryption-behavior` - - `detecting-suspicious-powershell-execution` - - `implementing-anti-ransomware-group-policy` - - `implementing-ransomware-kill-switch-detection` - - `testing-ransomware-recovery-procedures` - - `validating-backup-integrity-for-recovery` (also missing SKILL.md) - -- Directories missing agent.py: **9** (same set as above) - ---- - -## Dependency Audit - -### Top 30 Imports (by frequency across 733 agent.py files) - -| Package | Count | Type | Status | -|---------|-------|------|--------| -| json | 689 | stdlib | Safe | -| argparse | 514 | stdlib | Safe | -| sys | 421 | stdlib | Safe | -| subprocess | 222 | stdlib | Safe (see shell=True findings) | -| os | 219 | stdlib | Safe | -| re | 197 | stdlib | Safe | -| logging | 133 | stdlib | Safe | -| hashlib | 95 | stdlib | Safe | -| requests | 82 | PyPI | Safe, well-known | -| csv | 46 | stdlib | Safe | -| time | 40 | stdlib | Safe | -| datetime | 32 | stdlib | Safe | -| math | 31 | stdlib | Safe | -| struct | 30 | stdlib | Safe | -| socket | 27 | stdlib | Safe | -| base64 | 22 | stdlib | Safe | -| xml | 19 | stdlib | Safe | -| urllib/urllib3 | 28 | stdlib/PyPI | Safe | -| boto3 | 15 | PyPI | Safe, AWS SDK | -| ssl | 12 | stdlib | Safe | -| email | 12 | stdlib | Safe | -| hmac | 9 | stdlib | Safe | -| splunklib | 8 | PyPI | Safe, Splunk SDK | -| uuid | 7 | stdlib | Safe | -| collections | 7 | stdlib | Safe | -| sqlite3 | 6 | stdlib | Safe | -| pandas | 6 | PyPI | Safe | - -**Typosquatted packages found: 0** -**Known-malicious packages found: 0** -**Suspicious single-use packages found: 0** -**Packages not on PyPI found: 0** - -All imports are well-known standard library modules or established PyPI packages (requests, boto3, splunklib, pandas, pefile, yara-python, python-nmap, sslyze, ldap3, etc.). No evidence of supply chain compromise. - ---- - -## Content Verification - -### Methodology -30 randomly selected skills across 10 categories (forensics, cloud, network, malware, web, endpoint, SIEM, appsec, identity, threat intel) were verified by reading both SKILL.md and agent.py, then cross-referencing tool commands, API methods, CLI flags, and MITRE ATT&CK IDs against official documentation via web search. - -### Results - -| Category | Count | Verdict | -|----------|-------|---------| -| VERIFIED (all code references real tools/APIs) | 26/30 | 87% | -| PARTIALLY_REAL (SKILL.md real, agent.py generic boilerplate) | 4/30 | 13% | -| FAKE (invented commands/APIs) | 0/30 | 0% | - -**Key verification highlights:** -- All Volatility 3 plugin names confirmed real (windows.pslist, windows.psscan, windows.malfind) -- All Splunk SDK classes confirmed real (splunklib.client.connect, JSONResultsReader) -- All AWS CLI/boto3 commands verified (GuardDuty, CloudTrail, S3) -- All nmap flags verified against nmap.org documentation -- All sslyze classes confirmed against official docs -- All MITRE ATT&CK technique IDs verified (T1055.012, T1140, T1218.005, etc.) -- All Kubernetes commands verified against kubernetes.io -- All LDAP OIDs verified (1.2.840.113556.1.4.1941 for recursive group membership) -- LOLBin signatures verified against LOLBAS project -- Certipy/Certify commands verified for AD CS ESC1 exploitation - -**PARTIALLY_REAL pattern:** 4 skills use a generic HTTP-request template in agent.py (`GET {target}/api/v1/status` with bearer token) instead of implementing the actual tool described in SKILL.md. Examples: `implementing-semgrep-for-custom-sast-rules`, `performing-dark-web-monitoring-for-threats`. This suggests template-based generation was used for a subset of agent.py files. - ---- - -## Duplicate Analysis - -### Methodology -Jaccard similarity analysis across all 742 skill directory names, comparing SKILL.md content. - -### Results -- **Exact duplicates: 0** -- **Near-duplicate pairs (Jaccard >= 0.60): 67** - - Classified as REDUNDANT: **21 pairs** - - Classified as UNIQUE_TECHNIQUES (overlapping topic but different approach): **46 pairs** - -The 21 redundant pairs likely result from skills being created under slightly different names covering the same tool or technique. These should be reviewed for consolidation. - ---- - -## Folder Anatomy - -### Expected structure per skill: -``` -skill-name/ - SKILL.md - scripts/ - agent.py -``` - -### Completion Stats - -| Component | Present | Missing | Percentage | -|-----------|---------|---------|------------| -| Total directories | 742 | -- | -- | -| SKILL.md | 734 | 8 | 98.9% | -| scripts/ directory | 742 | 0 | 100% | -| scripts/agent.py | 733 | 9 | 98.8% | -| Fully complete (SKILL.md + agent.py) | 731 | 11 | 98.5% | -| Empty shell directories (scripts/ only) | 8 | -- | 1.1% | -| Partial (missing one file) | 3 | -- | 0.4% | - -Per auditor-13: 731 of 742 directories are fully complete (98.5%). 8 directories are empty shells containing only a scripts/ directory with no SKILL.md or agent.py. 3 directories are partial (have one file but not the other). The incomplete directories are predominantly from a ransomware/recovery-related batch addition. - ---- - -## Statistics - -| Category | Count | -|----------|-------| -| Total skill directories | 742 | -| Directories with SKILL.md | 734 (98.9%) | -| Directories with agent.py | 733 (98.8%) | -| SKILL.md frontmatter present | 734/734 (100%) | -| SKILL.md with extended frontmatter (extra fields) | 732/734 (99.7%) | -| SKILL.md frontmatter YAML parse errors | 2 | -| SKILL.md name field valid (lowercase-hyphens, <64 chars) | 734/734 (100%) | -| SKILL.md description field valid (<1024 chars) | 734/734 (100%) | -| Average SKILL.md length | 218 lines | -| Average agent.py length | 178 lines | -| Total Python code | 130,466 lines | -| Code security issues (CRITICAL -- eval/exec/pickle) | 0 | -| Code security issues (HIGH -- shell=True) | 25 | -| Code security issues (HIGH -- missing disclaimers) | 9 (both files) | -| Code security issues (MEDIUM -- SQL injection) | 6 | -| Dynamic imports (__import__) | 8 | -| verify=False (disabled SSL) | 178 | -| HTTP requests without timeout | 33 | -| HTTP URLs (not HTTPS) | 76 | -| Default credentials in code | ~9 | -| Prompt injection found | 0 (21 educational references) | -| YAML injection found | 0 | -| Hardcoded real secrets found | 0 | -| Typosquatted/malicious imports | 0 | -| Unique packages imported | 84 (all legitimate) | -| Skills verified as real code (sample) | 26/30 (87%) | -| Skills verified as partially real (sample) | 4/30 (13%) | -| Skills verified as fake | 0/30 (0%) | -| Exact duplicate skills | 0 | -| Near-duplicate (redundant) skill pairs | 21 | -| Overlap clusters | 4 | -| Complete folder anatomy | 731/742 (98.5%) | -| Empty shell directories | 8 | -| Partial directories | 3 | -| SKILL.md using alternate template | 697/734 (95%) | -| Stub SKILL.md files (<20 lines) | 10 | -| Placeholder text in SKILL.md | 0 | -| Offensive skills missing any disclaimer | 36/58 (62%) | - ---- - -## Recommendations - -### Priority 1 (HIGH): Fix shell injection patterns -Replace all 25 instances of `subprocess.run(cmd, shell=True)` with list-based commands and `shlex.split()`. This is especially urgent for the 4 files using f-string interpolation of file paths into shell commands (analyzing-linux-elf-malware, analyzing-network-traffic-for-incidents, performing-threat-emulation-with-atomic-red-team, performing-privilege-escalation-assessment). - -### Priority 2 (HIGH): Add authorized-testing disclaimers to all 58 offensive skills -9 skills have zero disclaimers. 36 of 58 offensive skills are missing at least one disclaimer. Every offensive skill should have a clear disclaimer in both SKILL.md and agent.py stating: "For authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have permission to test is illegal." - -### Priority 3 (MEDIUM): Fix SSL verification and add timeouts -178 instances of `verify=False` disable SSL certificate validation. 33 HTTP requests lack timeouts. Add `timeout=30` to all HTTP calls and only disable SSL verification when explicitly connecting to local/lab instances with self-signed certificates. - -### Priority 4 (MEDIUM): Complete the 11 incomplete skill directories -8 directories are empty shells and 3 are partial (missing either SKILL.md or agent.py). Either complete these skills or remove the incomplete directories. - -### Priority 5 (LOW): Consolidate 21 redundant skill pairs -Review and merge or differentiate the 21 near-duplicate skill pairs to reduce redundancy and improve navigability. - ---- - -## Final Verdict - -### Is this repo "vibe coded"? - -**No.** This is not vibe-coded. The evidence strongly indicates this is a carefully structured, systematically generated cybersecurity skills repository: - -- **87% of sampled skills contain verified, accurate tool commands, API methods, CLI flags, and MITRE ATT&CK references** confirmed against official documentation -- **0% contain fabricated or invented tool commands** -- even the 13% classified as "partially real" have accurate SKILL.md content, just generic agent.py boilerplate -- **130,466 lines of Python** with an average of 178 lines per agent.py -- these are non-trivial implementations, not stubs -- **734 SKILL.md files** averaging 218 lines each with consistent frontmatter and structured sections -- **Zero critical security vulnerabilities** (no eval/exec exploitation, no prompt injection, no real secrets, no YAML injection, no supply chain compromised packages) -- The entire import set consists of well-known, legitimate packages - -The repository shows hallmarks of systematic, high-quality generation with domain expertise: correct MITRE technique IDs, accurate tool-specific CLI flags, proper library usage patterns, and real-world security concepts. The 4/30 boilerplate agent.py files and the frontmatter consistency suggest automated generation with manual or expert-guided prompting, but the output quality is genuinely high. - -### Is it production-safe? - -**No, with caveats.** It is safe as a reference/educational resource but not safe to deploy directly: - -1. **25 shell injection risks** (shell=True with interpolation) would be exploitable if scripts ever receive untrusted input -2. **178 disabled SSL verifications** and **33 missing timeouts** are not production-grade -3. **342 files accept file paths without sanitization** -- acceptable for CLI tools, dangerous in any other context -4. **36 offensive tools lack proper legal disclaimers** -- a liability concern -5. The code was designed as educational/reference material, not as production software - -**Bottom line:** This is a high-quality, well-researched cybersecurity skills library with real, verified content and no critical vulnerabilities. It needs targeted hardening (shell injection, timeouts, disclaimers) before any production or public-facing use, but it is fundamentally sound educational material -- not a security risk in its intended context. - ---- - -*Report compiled by auditor-15 from findings of all 14 specialized audit agents (14/14 tasks completed).* -*Audit completed: 2026-03-17*