Security audit

Protocol Deviation Classifier

Security checks for vulnerabilities and agentic risk

Overview

The skill appears purpose-aligned and non-malicious, but it has review-worthy issues in a clinical compliance workflow: an unnecessary unpinned install step, under-documented output categories, and fail-open risk defaults that can understate serious deviations.

Review carefully before installing or using in a clinical quality workflow. Avoid running the pip install step unless the dependency list is fixed or removed, treat all outputs as advisory only, and require human QA review especially for missing or malformed risk-factor inputs and any downstream process expecting only major/minor labels.

A.I.G

Vulnerability Patterns

Insecure DependenciesIntroduces malicious components through unsafe dependency sources
Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review

Findings (2)

T08 · Insecure Dependencies

Warning

Location: requirements.txt:1
Finding: Unnecessary and Unpinned PyPI Dependencies Create a Supply-Chain Risk
Content: View full analysis
Remediation: View remediation

T09 · Insecure Skill Coding Practices

Warning

Location: scripts/main.py:86
Finding: Missing Risk Factors Are Converted to Benign Values and Bypass Automatic Assessment
Content: View full analysis
'DeviationEvent': """从字典创建事件""" def parse_risk(value): if isinstance(value, str): try: return RiskLevel(value.lower()) except ValueError: return RiskLevel.NONE return RiskLevel.NONE factors = data.get('severity_factors', {}) return cls( id=data.get('id', ''), description=data.get('description', ''), deviation_type=data.get('type', data.get('deviation_type', '')), occurrence_date=data.get('occurrence_date'), site_id=data.get('site_id'), subject_id=data.get('subject_id'), safety_impact=parse_risk(factors.get('safety_impact', 'none')), data_impact=parse_risk(factors.get('data_impact', 'none')), scientific_impact=parse_risk(factors.get('scientific_impact', 'none')) ) ``` `scripts/main.py:320-339`: ```python def classify_batch(self, events: List[Dict]) -> List[ClassificationResult]: """ 批量分类偏差事件 Args: events: 偏差事件字典列表 Returns: List[ClassificationResult]: 分类结果列表 """ results = [] for event_data in events: event = DeviationEvent.from_dict(event_data) result = self.classify( description=event.description, deviation_type=event.deviation_type, event_id=event.id, safety_impact=event.safety_impact, data_impact=event.data_impact, scientific_impact=event.scientific_impact ) results.append(result) return results ``` `scripts/main.py:732-740`: ```python classify_parser.add_argument("--safety-impact", ...[truncated 3378 chars]
Remediation: View remediation

SkillSpector

Vulnerability Patterns

Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands

Findings (9)

Tp4

High

Category: MCP Tool Poisoning
Confidence: 94% confidence
Finding: The documented purpose says the skill performs a binary major/minor classification, but the content also describes critical deviations, report generation, and interactive workflows. This mismatch can cause agents or users to invoke the skill under incorrect assumptions, leading to over-broad behavior, unsafe automation, or outputs being used for regulatory decisions beyond the intended scope.
Content: No source excerpt is available for this finding.

Undeclared Tool Scope

Medium

Category: MCP Least Privilege
Confidence: 91% confidence
Finding: The skill documentation acknowledges file read/write behavior but does not declare an explicit tool scope such as permissions or allowed-tools. This creates an authorization gap where an agent runtime may grant broader filesystem access than reviewers or users expect, increasing the chance of unintended data access or overwriting files.
Content: No source excerpt is available for this finding.

Vague Triggers

Medium

Category: Not specified by scanner
Confidence: 95% confidence
Finding: The trigger text says the skill should be used 'when classification assessment of protocol deviations is needed,' which is a general condition rather than a specific invocation scope. It does not define precise trigger phrases, boundaries, or exclusion conditions, so it could overlap with many routine discussions of clinical trial deviations.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium

Category: Not specified by scanner
Confidence: 93% confidence
Finding: This Python skill includes natural-language descriptions and operational output primarily in Chinese, and the interactive/CLI experience continues that fixed-language behavior elsewhere in the file. Under the policy rule for language/locale, forcing a specific language without user opt-in is a violation unless the locale constraint is clearly documented and justified.
Content: No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium

Category: Not specified by scanner
Confidence: 98% confidence
Finding: The manifest says the skill determines whether an incident is a 'major deviation' or 'minor deviation', but the implementation introduces and emits a third 'critical' outcome. This interface/behavior mismatch can break downstream automation, regulatory workflows, or policy logic that assumes a closed two-class decision, causing misrouting or unhandled cases in a clinical compliance context.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 78% confidence
Finding: The feature list advertises 'Chinese Support: Full support for Chinese clinical trial scenarios,' which introduces a locale-specific behavior without stating whether users can choose other languages or locales. Under the policy, locale constraints should be opt-in or clearly justified as region-specific.
Content: No source excerpt is available for this finding.

Unpinned Dependencies

Low

Category: Supply Chain
Confidence: 60% confidence
Finding: Dependencies lack version pinning, allowing potential malicious package updates. Consider pinning versions.
Content: Scanner excerpt · requirements.txt (reported line 1)May include surrounding context.
text
dataclasses enum

Unpinned Dependencies

Low

Category: Supply Chain
Confidence: 60% confidence
Finding: Dependencies lack version pinning, allowing potential malicious package updates. Consider pinning versions.
Content: Scanner excerpt · requirements.txt (reported line 2)May include surrounding context.
text
dataclasses enum

Description-Behavior Mismatch

Low

Category: Not specified by scanner
Confidence: 84% confidence
Finding: The manifest centers on classifying a deviation incident and assessing its impact, but this code also produces summary reports, category breakdowns, rates, and portfolio-level recommendations across batches of incidents. That is broader quality-management reporting functionality, not just per-incident classification.
Content: No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.