Returns Reverse Logistics

• Outcome: Pass
• Latest audit: Sep 11, 2026 · 12:04 AM UTC
• Version: 1.0.0

Overview

This skill is a coherent returns-operations reference, but users should apply privacy controls and human approval before acting on refunds, fraud holds, or customer data.

Install is reasonable for teams handling retail returns, but treat it as decision support. Verify recipients, minimize or mask customer data, keep fraud notes internal, follow regional law and company policy, and require human approval before issuing refunds, denying claims, suspending sellers, reporting customers, or running the eval script.

A.I.G

Vulnerability Patterns

• Insecure Dependencies: Introduces malicious components through unsafe dependency sources
• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally

Findings (1)

T08 · Insecure Dependencies

Error

• Location: evals/run_evals.py:7

Finding

Unverified External Python Module Import Enables Arbitrary Code Execution

Content

View full analysis

Vulnerability Details

File Location: evals/run_evals.py:7-10
Vulnerability Type: Unsafe external dependency loading through Python path manipulation
Risk Level: High

Vulnerable Code:

python

REPO_ROOT = Path(__file__).resolve().parent.parent.parent.parent
sys.path.insert(0, str(REPO_ROOT / "shared"))

from eval_framework import main

Technical Analysis

The evaluation runner computes a directory outside the audited project, prepends its shared subdirectory to sys.path, and imports eval_framework from that search path. Python executes a module's top-level code when importing it.

The imported dependency is not included in the audited project and is not constrained by a package version, trusted installation location, lockfile, signature, or cryptographic integrity check. Prepending the directory gives it priority over later module search locations. Consequently, a malicious or compromised eval_framework.py or equivalent importable package in the computed shared directory would execute automatically when the evaluation runner starts.

Exploitation requires an attacker to control or modify the external shared directory or its deployment contents. The audit did not establish that this directory is attacker-writable, so this prerequisite is environment-dependent.

Attack Path

1. The attacker gains write access to the shared directory derived four parent levels above evals/run_evals.py.
2. The attacker creates or replaces eval_framework.py, or an equivalent importable eval_framework package, with malicious top-level Python code.
3. A user or automated service executes python evals/run_evals.py.
4. The script inserts the attacker-controlled directory at index zero of sys.path.
5. Python resolves from eval_framework import main to the attacker's module.
6. The malicious top-level code executes with the privileges and environment of the evaluation process before `main() ...[truncated 586 chars]

Remediation

View remediation

Remediation Suggestions

1. Package eval_framework as a normal, trusted dependency and pin its exact version and hashes in a lockfile or requirements file.
2. Prefer an audited dependency installed into an isolated virtual environment rather than modifying sys.path at runtime.
3. If the framework is project-specific, vendor its reviewed source within the repository and import it through an explicit package structure.
4. If loading from an external directory is unavoidable:
   • Resolve and validate the canonical module path before execution.
   • Restrict the source directory to a trusted, administrator-controlled location.
   • Verify the module against a cryptographic digest or signature.
   • Reject symlinks and unexpected package layouts where applicable.
5. Run the evaluator in a least-privileged sandbox with no unnecessary credentials, restricted filesystem access, and limited network access.
6. Add CI checks that fail when imports resolve outside approved dependency roots.

SkillSpector

Vulnerability Patterns

• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep
• System Prompt Leakage: Direct Leakage, Indirect Extraction, Tool-Based Exfiltration
• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger

Findings (21)

Direct Prompt Extraction

High

• Category: System Prompt Leakage
• Confidence: 85% confidence

Finding

Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.

Content

Scanner excerpt · evals/results/baseline_results.json (reported line 19)May include surrounding context.

json

"weight": 0.25,
          "rating": "pass",
          "score": 1.0,
          "reasoning": "Agent approves the RMA without hesitation, correctly identifying all key factors: within 30-day window (12 days), valid return reason, high-value customer ($4,200 LTV), and acceptable return rate (12%). Provides clear next steps including RMA label and return instructions, and communicates expected refund timeline (3-5 business days). No inappropriate fraud flags despite the subjective return reason."
        },
        {
          "criterion": "restocking_fee",

Direct Prompt Extraction

High

• Category: System Prompt Leakage
• Confidence: 85% confidence

Finding

Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.

Content

Scanner excerpt · evals/results/baseline_results.json (reported line 44)May include surrounding context.

json

"weight": 0.25,
          "rating": "pass",
          "score": 1.0,
          "reasoning": "Agent approves the RMA without hesitation, correctly identifying all key factors: within 30-day window (12 days), valid return reason, high-value customer ($4,200 LTV), and acceptable return rate (12%). Provides clear next steps including RMA label and return instructions, and communicates expected refund timeline (3-5 business days). No inappropriate fraud flags despite the subjective return reason."
        },
        {
          "criterion": "restocking_fee",

Direct Prompt Extraction

High

• Category: System Prompt Leakage
• Confidence: 85% confidence

Finding

Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.

Content

Scanner excerpt · evals/results/baseline_results.json (reported line 76)May include surrounding context.

json

"weight": 0.25,
          "rating": "pass",
          "score": 1.0,
          "reasoning": "Agent approves the RMA without hesitation, correctly identifying all key factors: within 30-day window (12 days), valid return reason, high-value customer ($4,200 LTV), and acceptable return rate (12%). Provides clear next steps including RMA label and return instructions, and communicates expected refund timeline (3-5 business days). No inappropriate fraud flags despite the subjective return reason."
        },
        {
          "criterion": "restocking_fee",

Direct Prompt Extraction

High

• Category: System Prompt Leakage
• Confidence: 85% confidence

Finding

Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.

Content

Scanner excerpt · evals/results/baseline_results.json (reported line 470)May include surrounding context.

json

"weight": 0.25,
          "rating": "pass",
          "score": 1.0,
          "reasoning": "Agent approves the RMA without hesitation, correctly identifying all key factors: within 30-day window (12 days), valid return reason, high-value customer ($4,200 LTV), and acceptable return rate (12%). Provides clear next steps including RMA label and return instructions, and communicates expected refund timeline (3-5 business days). No inappropriate fraud flags despite the subjective return reason."
        },
        {
          "criterion": "restocking_fee",

Direct Prompt Extraction

High

• Category: System Prompt Leakage
• Confidence: 85% confidence

Finding

Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.

Content

Scanner excerpt · evals/results/baseline_results.md (reported line 44)May include surrounding context.

md

"weight": 0.25,
          "rating": "pass",
          "score": 1.0,
          "reasoning": "Agent approves the RMA without hesitation, correctly identifying all key factors: within 30-day window (12 days), valid return reason, high-value customer ($4,200 LTV), and acceptable return rate (12%). Provides clear next steps including RMA label and return instructions, and communicates expected refund timeline (3-5 business days). No inappropriate fraud flags despite the subjective return reason."
        },
        {
          "criterion": "restocking_fee",

Direct Prompt Extraction

High

• Category: System Prompt Leakage
• Confidence: 85% confidence

Finding

Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.

Content

Scanner excerpt · evals/scenarios.json (reported line 36)May include surrounding context.

json

"weight": 0.25,
          "rating": "pass",
          "score": 1.0,
          "reasoning": "Agent approves the RMA without hesitation, correctly identifying all key factors: within 30-day window (12 days), valid return reason, high-value customer ($4,200 LTV), and acceptable return rate (12%). Provides clear next steps including RMA label and return instructions, and communicates expected refund timeline (3-5 business days). No inappropriate fraud flags despite the subjective return reason."
        },
        {
          "criterion": "restocking_fee",

Anti-Refusal Statement

High

• Category: Anti-Refusal
• Confidence: 85% confidence

Finding

Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.

Content

Scanner excerpt · evals/scenarios.json (reported line 809)May include surrounding context.

json

"criterion": "return_processing",
          "weight": 0.25,
          "rubric": {
            "pass": "Process the return. All items are within the 30-day window. Grade A items (12): full refund, no restocking fee (tags on = restockable). Grade B items (3): full refund — tags removed on apparel within window is accepted by most retailers; makeup on collar is a try-on hazard, not intentional damage. Grade C shoes (1): apply restocking fee — outdoor sole wear goes beyond trying on. Net refund: ~$2,595 (full refund minus ~$35 restocking on the shoes). Do NOT refuse the return or apply blanket fees as retaliation for the content creation pattern.",
            "fail": "Refuses the entire return. Applies punitive restocking fees across all 16 items. Alternatively, accepts everything including the worn shoes without any restocking fee."
          }
        },

Vague Triggers

Medium

• Category: Not specified by scanner
• Confidence: 88% confidence

Finding

The manifest description says to use the skill when handling product returns, refund decisions, fraud detection, or warranty claims, but it does not define specific trigger phrases, boundaries, or exclusions. In a markdown/manifest context, this is broad enough to overlap with many ordinary customer-service conversations and may cause unintended invocation.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium

• Category: Not specified by scanner
• Confidence: 90% confidence

Finding

The file recommends monitoring a named customer's social media after a policy change, which introduces privacy-sensitive surveillance behavior without consent, minimization, or a defined legal basis. In a returns/fraud workflow, normalizing off-platform monitoring can lead to over-collection of personal data, discriminatory profiling, and compliance issues under privacy laws and internal acceptable-use policies.

Content

No source excerpt is available for this finding.

Autonomous Decision Making

Medium

• Category: Excessive Agency
• Confidence: 75% confidence

Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · evals/results/eval_results.json (reported line 809)May include surrounding context.

json

**refund_decision:** The response grants the exception and provides a refund, which is correct, but deducts $150 for 'refurbishment cost' resulting in a net refund of $2,345 instead of the full $2,495. While it mentions this is a 'standard refund' and acknowledges the compelling medical circumstances, the deduction seems inappropriate for a compassionate medical exception with documented hospitalization. The rubric expects full refund to original payment method without restocking fees for such compelling circumstances. The response does document the exception rationale adequately.

**subscription_and_logistics:** The response thoroughly addresses both subscription and logistics issues. It recommends immediate cancellation of the Peloton subscription with prorated refund calculation (~$117 for 3 months of unused service during hospitalization), acknowledging the customer was paying for unused service. For logistics, it arranges 'white-glove pickup at no charge' and mentions this normally costs $250, appropriately handling the 140 lb bike return without asking the customer to transport it herself.

**disposition:** The response properly addresses disposition by routing the bike to 'certified pre-owned program at $1,995-2,195 (80-88% of retail)' which aligns with the strong Peloton resale market. It mentions refurbishment including 'deep clean, inspection, minor wear from 4 uses' and provides realistic recovery calculations. While it doesn't explicitly mention de-linking the customer's account, the overall disposition strategy is sound for a Grade A-B Peloton Bike+ with minimal usage.

Autonomous Decision Making

Medium

• Category: Excessive Agency
• Confidence: 75% confidence

Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · evals/results/eval_results.md (reported line 470)May include surrounding context.

md

**refund_decision:** The response grants the exception and provides a refund, which is correct, but deducts $150 for 'refurbishment cost' resulting in a net refund of $2,345 instead of the full $2,495. While it mentions this is a 'standard refund' and acknowledges the compelling medical circumstances, the deduction seems inappropriate for a compassionate medical exception with documented hospitalization. The rubric expects full refund to original payment method without restocking fees for such compelling circumstances. The response does document the exception rationale adequately.

**subscription_and_logistics:** The response thoroughly addresses both subscription and logistics issues. It recommends immediate cancellation of the Peloton subscription with prorated refund calculation (~$117 for 3 months of unused service during hospitalization), acknowledging the customer was paying for unused service. For logistics, it arranges 'white-glove pickup at no charge' and mentions this normally costs $250, appropriately handling the 140 lb bike return without asking the customer to transport it herself.

**disposition:** The response properly addresses disposition by routing the bike to 'certified pre-owned program at $1,995-2,195 (80-88% of retail)' which aligns with the strong Peloton resale market. It mentions refurbishment including 'deep clean, inspection, minor wear from 4 uses' and provides realistic recovery calculations. While it doesn't explicitly mention de-linking the customer's account, the overall disposition strategy is sound for a Grade A-B Peloton Bike+ with minimal usage.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 92% confidence

Finding

The templates normalize inclusion of personal and transactional data such as customer names, emails, order numbers, payment method details, serial numbers, and claim identifiers, but provide no privacy or recipient-verification guardrails. In an operational setting, this increases the chance that users send unnecessary sensitive data to the wrong recipient or through inappropriate channels, creating avoidable data exposure risk.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 95% confidence

Finding

The fraud-review section contains highly sensitive internal investigative data, including fraud score, signals, customer LTV, review actions, and operational instructions. Although labeled as an internal companion note, it lacks strong handling warnings or structural safeguards, so it could be copied into customer-facing communications or shared externally, exposing detection logic and sensitive customer profiling data.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 91% confidence

Finding

The vendor and manufacturer claim templates encourage sharing customer identifiers, order references, and serial numbers with third parties without any instruction to confirm contractual, legal, or policy authorization for that disclosure. In returns and warranty workflows, this can lead to over-sharing of personal or device-linked data beyond what is necessary for claim processing.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium

• Category: Not specified by scanner
• Confidence: 89% confidence

Finding

The reference states that all thresholds, timelines, and cost assumptions reflect US retail and e-commerce operations. Because this is a general reference file and not clearly constrained to a US-only skill or workflow, it effectively forces a locale-specific framework without opt-in or explicit applicability checks.

Content

No source excerpt is available for this finding.

Autonomous Decision Making

Medium

• Category: Excessive Agency
• Confidence: 85% confidence

Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · references/decision-frameworks.md (reported line 608)May include surrounding context.

md

Customer initiates return on website/app
  │
  ├── Automated policy check (within window? excluded category? customer in good standing?)
  │     ├── Auto-approve → Generate RMA + prepaid return label
  │     ├── Auto-deny → Display denial reason + alternatives
  │     └── Manual review queue → Agent reviews within 4 hours
  │

Autonomous Decision Making

Medium

• Category: Excessive Agency
• Confidence: 85% confidence

Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · references/decision-frameworks.md (reported line 641)May include surrounding context.

md

**Key timing targets:**
| Step | Target | Stretch Goal |
|------|--------|-------------|
| RMA generation (auto-approve) | < 5 minutes | Instant |
| Return label delivery to customer | Immediate (email) | Immediate |
| Customer ship-back | < 7 days from RMA | < 5 days |
| Receiving scan at return centre | Day of delivery | Same as carrier delivery scan |

Autonomous Decision Making

Medium

• Category: Excessive Agency
• Confidence: 85% confidence

Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · references/decision-frameworks.md (reported line 723)May include surrounding context.

md

**Key timing targets:**
| Step | Target | Stretch Goal |
|------|--------|-------------|
| RMA generation (auto-approve) | < 5 minutes | Instant |
| Return label delivery to customer | Immediate (email) | Immediate |
| Customer ship-back | < 7 days from RMA | < 5 days |
| Receiving scan at return centre | Day of delivery | Same as carrier delivery scan |

Autonomous Decision Making

Medium

• Category: Excessive Agency
• Confidence: 85% confidence

Finding

Automatically approving returnless refunds for items under a value threshold can be exploited for fraud, especially if the decision is made solely from estimated shipping cost and product value. In this skill context, the workflow is explicitly intended to drive customer-facing refund decisions, so unsafe automation can directly cause financial loss at scale.

Content

Scanner excerpt · references/decision-frameworks.md (reported line 722)May include surrounding context.

text

Return shipping cost estimate > 40% of product value?
  ├── Yes → Evaluate returnless refund
  │     ├── Product value < $50 → Auto-approve returnless refund
  │     ├── Product value $50-100 → Supervisor auto-approve
  │     ├── Product value $100-200 → Manager review (consider partial return — just the defective component)
  │     └── Product value > $200 → Case-by-case (may justify return shipping for high-value)

Autonomous Decision Making

Medium

• Category: Excessive Agency
• Confidence: 75% confidence

Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · references/edge-cases.md (reported line 343)May include surrounding context.

md

The chargeback has regulatory timelines: Visa requires the merchant to respond within 20 days or the chargeback auto-closes in the cardholder's favour. The return has no such external deadline. This asymmetry means the chargeback demands attention first.

**Common Mistake:**
Processing the return refund without checking for an active chargeback. This is the #1 source of double-refund losses in e-commerce. The second mistake: immediately assuming fraud and antagonising a customer who may simply be confused about the process.

**Expert Approach:**
1. HALT the RMA process immediately. Add a "chargeback hold" flag to the RMA. Do not process a return refund while a chargeback is active.

Natural-Language Policy Violations

Low

• Category: Not specified by scanner
• Confidence: 91% confidence

Finding

This JSON file includes a customer communication template addressing the customer as "Dear Mr. Park,". Using a specific honorific and inferred gender without confirming the customer's preference can violate language/locale or personalization policy expectations, especially when no opt-in or preference source is stated.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.