chat-ai

• Outcome: Review
• Latest audit: Sep 11, 2026 · 10:06 AM UTC
• Version: 1.0.0

Overview

The skill has a clear text-to-SQL purpose, but it depends on unreviewed local project code from a hard-coded path before executing database-query logic.

Install only in a controlled environment after packaging or verifying the referenced Winner-Ai project code, pinning dependencies, and confirming that SQL execution is read-only or otherwise governed by explicit database permissions and user approval. This should not be treated as a self-contained reviewed skill in its current form.

A.I.G

Vulnerability Patterns

• Tool Hijacking and Spoofing: Modifies or replaces tools so legitimate-looking calls execute attacker logic
• 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

Findings (2)

T07 · Tool Hijacking and Spoofing

Error

• Location: main.py:11
• Finding: External Import-Path Injection Enables Execution of Unreviewed Local Code## Vulnerability Details **File Location**: `main.py:11-18` **Vulnerability Type**: `T07: Tool Hijacking and Spoofing` **Risk Level**: High **Complete Code Snippet**: ```python # Dynamic project path insertion PROJECT_ROOT = r"D:\javaworkspace\Winner-Ai" sys.path.insert(0, PROJECT_ROOT) from app.services.ai_chat_orchestrator import AIChatOrchestrator from app.schemas.text2sql import ResponseMessage from app.agents.base import StreamResponseCollector from app.core.redis_client import get_redis_client from app.core.json_encoder import clean_message_data ``` ### Technical Analysis The Skill prepends a hard-coded external location to `sys.path`, giving that location priority over normally resolved Python packages. It then imports multiple `app` modules that are not included in the audited project. Python executes module-level code during import. Consequently, the effective behavior of this Skill depends on unreviewed files at the resolved path. On systems where the Windows-style path does not represent an absolute Windows path, it may also be interpreted relative to the working environment, increasing the possibility that another user or process can create the expected package structure. This is a tool or module hijacking weakness: an attacker who can control the resolved path can provide a spoofed `app` package whose names match the expected imports. ### Attack Path 1. An attacker obtains write access to the hard-coded path or to the location where it resolves in the runtime environment. 2. The attacker creates or modifies an `app` package containing modules such as `services/ai_chat_orchestrator.py`. 3. The victim loads or invokes the Skill. 4. `sys.path.insert(0, PROJECT_ROOT)` places the attacker-controlled location at the front of the import search path. 5. Python resolves the imports from the spoofed package. 6. Attacker-controlled module initialization code executes with the privileges of the Skill pr ...[truncated 801 chars]
• Remediation: ## Remediation Suggestions - Package all required implementation modules inside the reviewed Skill artifact, or install them as a separately reviewed and integrity-verified package. - Do not prepend hard-coded or potentially writable directories to `sys.path`. - If local imports are required, derive the path from `Path(__file__).resolve()` and ensure it remains within the trusted package directory. - Verify the resolved origin of imported modules using `importlib.util.find_spec()` before importing them. - Reject modules whose resolved paths fall outside an approved, read-only directory. - Run the Skill under a dedicated least-privilege account with restricted filesystem, database, and network permissions. - Include the orchestration and SQL-execution implementation in future audits so its authorization and query-safety controls can be assessed.

T08 · Insecure Dependencies

Warning

• Location: requirements.txt:1
• Finding: Unsafe and Non-Reproducible Dependency Constraints## Vulnerability Details **File Location**: `requirements.txt:1-5` **Vulnerability Type**: `T08: Insecure Dependencies` **Risk Level**: Medium **Complete Code Snippet**: ```text langchain==0.1.x autogen==0.3.x sqlalchemy pandas pydantic>=2.0 ``` ### Technical Analysis `sqlalchemy` and `pandas` have no version constraints, while `pydantic` has only a lower bound. Dependency installation can therefore select releases that were not reviewed with this Skill, making builds non-reproducible and allowing future incompatible or compromised versions to enter the environment. The `langchain==0.1.x` and `autogen==0.3.x` constraints also do not use standard PEP 440 wildcard syntax. A compatible-release range or a wildcard ending in `.*` would normally be used. These malformed constraints may cause dependency resolution or installation to fail. No lock file or package-integrity hashes are provided, so the artifact does not establish the exact distributions expected at installation time. ### Attack Path 1. An operator installs the dependencies in a new or updated environment. 2. The package resolver processes unpinned or broadly bounded dependencies. 3. It selects versions that differ from those used during development or review, or it fails on the malformed constraints. 4. If an unexpectedly selected release contains a vulnerability, malicious change, or incompatible behavior, that code is installed into the Agent environment. 5. The dependency executes with the privileges of the Skill process when imported or invoked. This finding does not establish that any listed package is currently malicious. The risk results from the inability to reproduce and verify the dependency set. ### Impact Assessment The immediate impact includes installation failures and runtime incompatibility. If an unsafe dependency release is resolved, its code can execute with the Agent process's filesystem, environment, network, and application p ...[truncated 118 chars]
• Remediation: ## Remediation Suggestions - Replace all dependency declarations with exact versions that have been reviewed and tested. - Replace invalid `==0.1.x` and `==0.3.x` syntax with valid PEP 440 constraints; preferably use exact pins in the deployment lock file. - Generate a deterministic lock file that includes transitive dependencies. - Require package hashes during installation, such as with `pip install --require-hashes`. - Install only from an approved package index over authenticated TLS, and prevent fallback to untrusted indexes. - Run dependency vulnerability and license scans in CI, and update pins through a controlled review process. - Test the locked dependency set in a clean environment before release.

SkillSpector

Vulnerability Patterns

• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (7)

Missing User Warnings

High

• Confidence: 98% confidence
• Finding: The skill hard-codes the user approval callback to always return "同意", which bypasses any real confirmation step before potentially sensitive actions occur. In a text-to-SQL skill, this is especially dangerous because the orchestrator may interpret this as authorization to execute generated SQL, approve risky queries, or proceed past safety checkpoints without actual user consent.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

langchain==0.1.x
autogen==0.3.x
sqlalchemy
pandas
pydantic>=2.0

• Confidence: 97% confidence
• Finding: The dependency manifest leaves sqlalchemy unpinned, so installs may drift to different versions over time and may inadvertently resolve to vulnerable or breaking releases. In a skill that generates and executes SQL, dependency predictability matters more because ORM/query-layer security fixes and behavior changes can directly affect database safety.

Unverifiable Dependency: sqlalchemy has 6 known advisory(ies) (CVE-2019-7548 (SQLAlchemy is vulnerable to SQL Injection via group_by parameter ); CVE-2019-7164 (SQLAlchemy vulnerable to SQL Injection via order_by parameter); CVE-2012-0805 (SQLAlchemy vulnerable to SQL injection) +3 more), but the manifest does not pin a version, so it is unknown whether the installed release is affected

Low

• Category: Supply Chain
• Confidence: 88% confidence
• Finding: Sqlalchemy has multiple historical SQL injection advisories, and because the manifest does not pin a version, there is no way to verify that deployment avoids affected releases. This is more concerning in this skill than in a generic app because the core function is converting natural language into SQL and executing it, so any query-construction weakness in the stack has elevated relevance.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

langchain==0.1.x
autogen==0.3.x
sqlalchemy
pandas
pydantic>=2.0

• Confidence: 95% confidence
• Finding: The pandas requirement is unpinned, which makes builds non-reproducible and can introduce vulnerable or incompatible versions without code changes. While pandas is not the primary security boundary here, this skill processes data end-to-end, so unexpected dependency changes can still expand attack surface.

Unverifiable Dependency: pandas has 1 known advisory(ies) (CVE-2020-13091 (** DISPUTED ** pandas through 1.0.3 can unserialize and execute commands from an)), but the manifest does not pin a version, so it is unknown whether the installed release is affected

Low

• Category: Supply Chain
• Confidence: 76% confidence
• Finding: The manifest does not pin pandas, so it is impossible to confirm whether the installed version is affected by known advisories. Although the cited pandas issue is less central to this skill's primary risk than SQL execution, unresolved version ambiguity still creates avoidable supply-chain uncertainty.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

autogen==0.3.x
sqlalchemy
pandas
pydantic>=2.0

• Confidence: 96% confidence
• Finding: Using pydantic>=2.0 permits a wide range of future versions, including ones with undiscovered issues or breaking behavior, reducing supply-chain stability. Because pydantic often validates untrusted inputs in agent workflows, version drift can affect both reliability and exposure to parser/validation flaws.

Unverifiable Dependency: pydantic has 4 known advisory(ies) (CVE-2021-29510 (Use of "infinity" as an input to datetime and date fields causes infinite loop i); CVE-2024-3772 (Pydantic regular expression denial of service); CVE-2021-29510 (Pydantic is a data validation and settings management using Python type hinting.) +1 more), but the manifest does not pin a version, so it is unknown whether the installed release is affected

Low

• Category: Supply Chain
• Confidence: 80% confidence
• Finding: Because pydantic is not pinned, the deployment could resolve to versions impacted by known issues such as regex DoS or validation edge cases. In an agent skill that accepts natural-language user input and likely validates structured intermediate data, that uncertainty can affect availability and input-handling security.

Static analysis

No suspicious patterns detected.