Galileo python sdk

• Outcome: Pass
• Latest audit: Sep 16, 2026 · 5:47 AM UTC
• Version: 1.2.1

Overview

This skill is a documentation reference for Galileo's Python SDK, and its network logging behavior is expected for observability but should be used carefully with sensitive data.

Install and use this only in projects where sending evaluation and tracing data to Galileo is acceptable. Pin package versions in production, keep API keys in environment or secret storage, and avoid logging secrets, personal data, or regulated customer content unless your privacy and retention requirements allow it.

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

Warning

• Location: SKILL.md:47

Finding

Unpinned Third-Party Package Installation Creates Supply-Chain Risk

Content

View full analysis

Vulnerability Details

File Location: SKILL.md, lines 47-59
Vulnerability Type: Unpinned third-party dependencies
Risk Level: Medium

Vulnerable Code

bash

pip install galileo

For evaluation features with the legacy prompt engineering interface:

bash

pip install promptquality

For runtime guardrails:

bash

pip install galileo-protect

Technical Analysis

The primary installation instructions retrieve mutable latest versions of three third-party packages without version constraints, integrity hashes, or a locked dependency graph. Consequently, the code installed and executed can differ from the version reviewed when this Skill was authored.

Python package installation can execute package build logic, while imported packages subsequently execute with the privileges of the consuming application. If a package release, maintainer account, transitive dependency, or configured package index is compromised, following these instructions could introduce attacker-controlled code. Ordinary upstream changes may also alter telemetry behavior or introduce incompatible or vulnerable dependencies.

The separately documented promptquality==1.14.0 constraint in references/PROMPTQUALITY.md reduces version drift for that alternative installation path, but the main installation instructions remain unpinned and do not verify package integrity.

Attack Path

1. An attacker compromises a named package, one of its transitive dependencies, a maintainer publishing account, or the package index used by the environment.
2. The attacker publishes a malicious release that satisfies the unconstrained installation request.
3. A user or agent follows the documented pip install command.
4. pip resolves and downloads the attacker-controlled release because no reviewed version or hash is required.
5. Malicious build-time code executes during installation, or malicious runtime code executes w ...[truncated 695 chars]

Remediation

View remediation

Remediation Suggestions

1. Pin every direct dependency to a reviewed exact version, including galileo, promptquality, and galileo-protect.
2. Generate and maintain a lock file that also constrains transitive dependencies.
3. Record cryptographic hashes and install with pip --require-hashes where feasible.
4. Use a trusted or internally mirrored package index and explicitly configure the permitted index source.
5. Scan locked dependencies for known vulnerabilities and review updates before changing pins.
6. Install packages in an isolated virtual environment or container as a non-privileged user.
7. Reconcile the main installation guidance with the documented SDK compatibility matrix so users cannot accidentally install incompatible major versions.
8. Prefer a reviewed requirements file, for example:

bash

python -m pip install --require-hashes -r requirements.txt

The requirements file should contain approved exact versions and hashes rather than unconstrained package names.

SkillSpector

Vulnerability Patterns

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

Findings (8)

Credential Access

High

• Category: Privilege Escalation
• Confidence: 60% confidence

Finding

Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.

Content

Scanner excerpt · SKILL.md (reported line 89)May include surrounding context.

Set the following environment variables:

bash

# .env file or shell environment
GALILEO_API_KEY="your-api-key"            # Required — from Galileo console
GALILEO_CONSOLE_URL="https://app.galileo.ai"  # Console URL (or self-hosted URL)
GALILEO_PROJECT="my-project"              # Optional — default project

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 93% confidence

Finding

The skill prominently promotes automatic tracing, observability, and runtime uploads but does not clearly warn that prompts, model inputs/outputs, retrieved context, tool results, and metadata may be transmitted to Galileo services. In an LLM/observability SDK, this omission can cause developers to enable logging on sensitive workloads and unintentionally exfiltrate proprietary or regulated data.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The flush example instructs users to upload captured traces to Galileo but gives no adjacent disclosure that those traces may contain sensitive application data collected during prompts, responses, and span logging. Because the example is actionable and easy to copy, users may operationalize data export without understanding the privacy and compliance implications.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 93% confidence

Finding

The examples show logging prompts, contexts, queries, and model outputs to Galileo, which implies transmission of potentially sensitive application data to an external service. In SDK reference documentation, omitting any warning about data sensitivity, redaction, or consent can lead developers to copy these patterns into production and unintentionally expose proprietary, personal, or regulated data.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 94% confidence

Finding

The documentation states that calls are automatically logged to Galileo with full span details, but it does not warn users that prompts, model inputs/outputs, metadata, or other application data may be transmitted to an external service. In an observability SDK context, this can lead to unintentional disclosure of sensitive data because users may enable tracing without understanding the data-sharing implications.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 96% confidence

Finding

The exporter configuration sends OpenTelemetry traces to a remote Galileo endpoint using an API key, but the documentation does not clearly warn that trace payloads may include prompts, responses, tool inputs, and other sensitive telemetry. This omission increases the risk that developers will deploy the example as-is and exfiltrate confidential application or user data to a third-party service unintentionally.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The examples explicitly show sending inputs, contexts, and model outputs to Galileo for evaluation without any warning that these fields may contain sensitive user data, secrets, or regulated content. In an observability/evaluation SDK, users often paste real prompts and retrieval context into logging pipelines, so omission of privacy guidance can lead to unintended external transmission and retention of confidential data.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low

• Category: Not specified by scanner
• Confidence: 77% confidence

Finding

The authentication example directs users to connect to https://app.galileo.ai, but the markdown does not explain that subsequent evaluation operations may interact with a hosted service and involve remote data processing. For markdown files, omissions of warnings about behaviors affecting privacy should be flagged when the skill description normalizes external service use without disclosure.

Content

No source excerpt is available for this finding.