Alibabacloud Elasticsearch Log Config Generator

• Outcome: Review
• Latest audit: Jul 9, 2026 · 9:40 PM UTC
• Version: 0.0.1

Overview

This skill appears to help build log-ingestion configs, but some included templates and examples can expose unauthenticated services or high-privilege/destructive options if deployed as written.

Review the templates before installing or using them in production. Bind ingestion endpoints to localhost or private interfaces unless public exposure is required, enable TLS and authentication/HMAC, avoid Docker socket/root access where possible, do not enable source log deletion without backups and verified delivery, and redact or allowlist Kafka headers and other metadata before sending them to Elasticsearch.

SkillSpector

By NVIDIA

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code

Findings (12)

Vague Triggers

Medium

• Confidence: 96% confidence
• Finding: The template exposes a Filebeat http_endpoint on 0.0.0.0:8080 and accepts POSTed JSON on a configurable path, but all authentication, HMAC validation, and TLS protections are commented out. In a log-ingestion skill, this is especially dangerous because users may deploy the template as-is, allowing unauthenticated external parties to inject arbitrary log events, flood the endpoint, or poison downstream Elasticsearch data.

Vague Triggers

Medium

• Confidence: 96% confidence
• Finding: The template binds both OTLP gRPC and HTTP receivers to 0.0.0.0, exposing log ingestion interfaces on all network interfaces by default. In a configuration-generation skill, users may deploy this verbatim, which can unintentionally allow unauthorized log submission, abuse, or reconnaissance unless network controls and authentication are explicitly enabled.

Missing User Warnings

Medium

• Confidence: 88% confidence
• Finding: The documentation includes a delete-after-ingestion option that can permanently remove source log files after processing, but it does not clearly warn about the operational and forensic risks of data loss if ingestion is misconfigured, partial, or later disputed. In a log collection skill, this is materially dangerous because users may copy the example into production and destroy original evidence or break retention requirements.

Missing User Warnings

Medium

• Confidence: 93% confidence
• Finding: The example binds the Filebeat HTTP endpoint to 0.0.0.0, making the webhook listener reachable on all network interfaces. In a skill whose purpose is to generate production-ready Elasticsearch log collection configs, users may copy this example directly; without an explicit warning or strong authentication/TLS guidance nearby, this can expose an ingestion endpoint to unsolicited traffic, event injection, denial of service, or abuse from unintended networks.

Missing User Warnings

Medium

• Confidence: 93% confidence
• Finding: The documentation includes a minimal example using `http://localhost:9200` without explicitly warning that plain HTTP sends credentials and log data unencrypted if reused beyond a local test environment. In this skill’s context, users are asking for production-ready Elasticsearch log shipping configs, so insecure examples are likely to be copied into real deployments and can lead to interception or tampering of sensitive telemetry.

Missing User Warnings

Medium

• Confidence: 90% confidence
• Finding: The document explicitly instructs users to mount `/var/run/docker.sock` and run Filebeat as root in a container, but it does not warn that the Docker socket is effectively a root-equivalent control channel to the host. In a config-generation skill, this guidance can normalize an unsafe deployment pattern and lead users to grant excessive privileges to log collectors.

Missing User Warnings

Medium

• Confidence: 90% confidence
• Finding: The documentation states that all Kafka message headers are attached to request metadata, but it does not warn that headers commonly carry secrets, tokens, tenant identifiers, or other sensitive business data. In a log-ingestion skill that generates configs for forwarding data to Elasticsearch, this omission can lead users to propagate sensitive metadata into downstream storage and search systems, increasing exposure and retention risk.

Missing User Warnings

Medium

• Confidence: 91% confidence
• Finding: The header extraction section explains how to extract Kafka headers into resource attributes but omits any caution that these attributes may contain credentials, session tokens, PII, or internal routing data. Because this skill is specifically for generating telemetry/log configs that send data to Elasticsearch, users may unknowingly persist and index sensitive header content, broadening access to it.

Missing User Warnings

Medium

• Confidence: 89% confidence
• Finding: The document includes client TLS examples that explicitly use `insecure: true` and `insecure_skip_verify: true` without an adjacent warning that these settings disable or weaken certificate validation. In a skill that generates production logging configurations for Elasticsearch, such examples can be copied directly into output configs, enabling man-in-the-middle attacks or silent connection to untrusted endpoints.

Missing User Warnings

Medium

• Confidence: 93% confidence
• Finding: The sample configuration embeds a plaintext OAuth client secret and simultaneously shows `tls.insecure: true`, which can normalize unsafe defaults for users copying the example into production. In this skill context, which generates Elasticsearch/collector configs, documentation snippets are likely to be reused directly, increasing the chance of credential exposure and man-in-the-middle risk during token retrieval.

Missing User Warnings

Medium

• Confidence: 74% confidence
• Finding: The headers_setter example forwards request metadata such as tenant identifiers upstream without warning about sensitivity or trust boundaries. In a log-pipeline skill that generates production configs for Elasticsearch-related ingestion, this can lead users to propagate spoofable or sensitive metadata into upstream headers, causing tenant mix-ups, data leakage, or confused-deputy style authorization issues if the source metadata is not validated.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

| `cleanup_timeout` | duration | `60s` | Drop cached metadata after inactivity. |
| `labels.dedot` | bool | `true` | `.` → `_` in labels. |

Mount `/var/run/docker.sock` and run as root when Filebeat itself runs in a container.

## decode_json_fields

• Confidence: 94% confidence
• Finding: run as root

VirusTotal

64/64 vendors flagged this skill as clean.

Static analysis

Detected: suspicious.exposed_secret_literal

File appears to expose a hardcoded API secret or token.

Critical

• Code: suspicious.exposed_secret_literal
• Location: references/opentelemetry/15-configauth.md:42