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Empirically analyzes 31,132 agent skills to identify 14 distinct vulnerability patterns, finding 26.1% contain security flaws including data exfiltration, privilege escalation, and malicious intent risks that require mandatory vetting.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/ADu2021/skillXiv --skill agent-skills-security-analysis명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
SOC 직업 분류 기준
| name | agent-skills-security-analysis |
| title | Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.10338 |
| keywords | ["agent-security","skill-vetting","vulnerability-detection","threat-analysis","skill-marketplace"] |
| description | Empirically analyzes 31,132 agent skills to identify 14 distinct vulnerability patterns, finding 26.1% contain security flaws including data exfiltration, privilege escalation, and malicious intent risks that require mandatory vetting. |
Conduct security analysis of modular agent skills before deployment. Use multi-stage detection combining static analysis and LLM-based semantic classification to identify vulnerabilities such as data exfiltration, privilege escalation, prompt injection, and supply chain risks.
Implement multi-stage detection pipeline combining static and semantic analysis.
# Multi-stage detection framework
class SkillScan:
def __init__(self):
self.static_analyzer = StaticAnalyzer()
self.semantic_classifier = SemanticClassifier()
def scan_skill(self, skill_code, skill_metadata):
"""Comprehensive vulnerability detection"""
results = {
"static_findings": self.static_analyzer.analyze(skill_code),
"semantic_findings": self.semantic_classifier.classify(skill_code),
"metadata_issues": self.check_metadata(skill_metadata),
"vulnerability_patterns": []
}
# Consolidate findings
results["vulnerability_patterns"] = self.consolidate_findings(results)
results["risk_score"] = .compute_risk_score(results)
results
():
patterns = []
finding findings[]:
pattern = .classify_pattern(finding)
patterns.append(pattern)
((patterns))
Detect vulnerabilities through code pattern matching.
# Static analysis patterns
class StaticAnalyzer:
VULNERABILITY_PATTERNS = {
"credential_exposure": [
r"api[_]?key\s*=",
r"password\s*=",
r"token\s*="
],
"file_access_risk": [
r"open\s*\(",
r"read\s*file",
r"write\s*file",
r"os\.remove"
],
"network_calls": [
r"requests\.",
r"urllib",
r"socket\.",
r"send.*http"
],
"process_execution": [
r"subprocess\.",
r"os\.system",
r"popen"
]
}
def analyze(self, code):
"""Find suspicious patterns in code"""
findings = []
for pattern_type, patterns in self.VULNERABILITY_PATTERNS.items():
for pattern in patterns:
matches = re.findall(pattern, code, re.IGNORECASE)
if matches:
findings.append({
"type": pattern_type,
"pattern": pattern,
"match_count": len(matches),
"severity": self.estimate_severity(pattern_type)
})
return findings
def estimate_severity(self, pattern_type):
"""Assess severity of vulnerability pattern"""
severity_map = {
"credential_exposure": "critical",
"process_execution": "high",
"network_calls": "medium",
"file_access_risk": "medium"
}
return severity_map.get(pattern_type, "low")
Use language models to understand vulnerability intent.
# Semantic vulnerability classification
class SemanticClassifier:
def classify(self, code):
"""Identify vulnerability intent through semantic analysis"""
# Classify malicious intent categories
intent_categories = [
"data_exfiltration",
"privilege_escalation",
"prompt_injection",
"supply_chain_risk",
"benign_risky_pattern"
]
classifications = {}
for category in intent_categories:
prompt = f"""Analyze this code for {category} intent:
{code}
Is there evidence of {category}? (yes/no/unclear)
Confidence: 0-1
"""
result = self.llm_classify(prompt)
classifications[category] = {
"detected": result["answer"],
"confidence": result["confidence"]
}
return classifications
def llm_classify(self, prompt):
"""LLM-based semantic analysis"""
# Would use actual LLM API
pass
Organize vulnerabilities into actionable categories.
# Vulnerability categories with risk levels
class VulnerabilityCategory:
CATEGORIES = {
"data_exfiltration": {
"description": "Attempt to send data outside system",
"prevalence": 0.133, # 13.3% of vulnerable skills
"examples": ["send_logs", "collect_files", "api_exfil"]
},
"privilege_escalation": {
"description": "Attempt to gain higher permissions",
"prevalence": 0.118, # 11.8%
"examples": ["sudo_access", "admin_check", "permission_bypass"]
},
"malicious_intent": {
"description": "Clear evidence of deliberate harm",
"prevalence": 0.052, # 5.2%
"examples": ["backdoor", "ransomware_pattern", "botnet"]
},
"prompt_injection": {
"description": "Vulnerability to LLM prompt injection",
"prevalence": 0.045,
"examples": ["unescaped_input", "eval_user_input"]
},
"supply_chain": {
"description": "Risk to dependency management",
"prevalence": 0.035,
"examples": ["typosquatting", "dependency_confusion"]
}
}
@staticmethod
def get_risk_level(vulnerability):
"""Map vulnerability to risk level"""
if vulnerability in ["data_exfiltration", "privilege_escalation", "malicious_intent"]:
return "high"
elif vulnerability in ["prompt_injection"]:
return "medium"
else:
return "low"
Assign quantitative risk scores to skills.
# Risk scoring
class RiskScorer:
def compute_score(self, findings):
"""Compute overall risk score 0-1"""
if not findings:
return 0.0
# Weighted scoring
critical_count = len([f for f in findings if f.get("severity") == "critical"])
high_count = len([f for f in findings if f.get("severity") == "high"])
medium_count = len([f for f in findings if f.get("severity") == "medium"])
score = (
critical_count * 0.5 +
high_count * 0.3 +
medium_count * 0.1
) / len(findings)
return min(1.0, score)
def requires_vetting(self, risk_score):
"""Determine if skill requires manual review"""
return risk_score > 0.3
Identify that executable scripts increase vulnerability risk.
# Script bundling risk factor
class BundlingAnalysis:
def assess_bundling_risk(self, skill):
"""Check if skill includes executable scripts"""
has_scripts = any(
script_ext in skill["files"]
for script_ext in [".py", ".sh", ".js", ".exe"]
)
if has_scripts:
# Scripts are 2.12x more likely to contain vulnerabilities
return {"has_scripts": True, "risk_multiplier": 2.12}
else:
return {"has_scripts": False, "risk_multiplier": 1.0}