Code Remediation with Gomboc.ai Community Edition

• Outcome: Review
• Latest audit: Sep 15, 2026 · 1:39 AM UTC
• Version: 0.2.5

Overview

This skill has a coherent security-remediation goal, but its local tools and deployment examples are overclaimed and under-scoped in ways users should review carefully before installing.

Install only if you are comfortable reviewing and tightening the integration first: pin container/action versions, bind MCP to localhost, remove automatic restart unless needed, store GOMBOC_PAT in a proper secret manager, avoid relying on the local CLI as proof that remediation occurred, and require human review before any commit or push workflow.

A.I.G

Vulnerability Patterns

• Unauthorized Access and Privilege Escalation: Obtains permissions beyond the task's legitimate needs
• Tool Hijacking and Spoofing: Modifies or replaces tools so legitimate-looking calls execute attacker logic
• Insecure Dependencies: Introduces malicious components through unsafe dependency sources
• Insecure Skill Coding Practices: Finds exploitable flaws such as hardcoded secrets or command injection
• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads

Findings (5)

T07 · Tool Hijacking and Spoofing

Warning

• Location: scripts/cli-wrapper.py:145

Finding

CLI Reports Scanning and Remediation Without Performing the Advertised Operations

Content

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Vulnerability Details

File Location: scripts/cli-wrapper.py:145-164, scripts/cli-wrapper.py:205-238, and scripts/cli-wrapper.py:245-295
Vulnerability Type: Tool behavior spoofing and misleading success reporting
Risk Level: Medium

The affected behavior is represented by the following code:

python

if args.path and Path(args.path).exists():
    files = list(Path(args.path).rglob("*"))
    code_files = [f for f in files if f.is_file() and f.suffix in 
                 ['.py', '.tf', '.json', '.yaml', '.yml', '.js', '.ts']]

    print(f"\n📊 Repository Statistics:")
    print(f"   Total files: {len(files)}")
    print(f"   Code files: {len(code_files)}")

python

result = call_gomboc_api(query)
account = result.get("account", {})
fix_events = account.get("fixEvents", {}).get("edges", [])

if fix_events:
    print(f"\n✅ Generated {len(fix_events)} fix event(s)")

python

result = call_gomboc_api(query)
account = result.get("account", {})
fix_events = account.get("fixEvents", {}).get("edges", [])

if fix_events:
    print(f"   Applied {len(fix_events)} remediation(s)")
else:
    print(f"   No remediations available")

print(f"\n✅ Remediation complete")

if args.commit:
    print(f"   (In production: auto-commits are handled by GitHub)")

Technical Analysis

The scan command enumerates local files but does not submit their content or the supplied path to a scanning operation. The fix and remediate commands query existing account-level fix events but do not generate fixes, modify files, create commits, or push changes.

Despite this, the commands print messages such as “Generated,” “Applied,” and “Remediation complete.” These messages conflict with the documented claim that the wrapper performs local scanning and remediation. An automated caller cannot reliably distinguish actual remediation from a metadata query.

This is a tool-spoofing risk because a legitimate-looking security tool interface returns ...[truncated 984 chars]

Remediation

View remediation

Remediation Suggestions

• Implement actual scan, fix-generation, and remediation API operations matching the documented interface.
• Pass an authenticated repository identifier or supported scan input rather than merely counting local files.
• Verify that returned events belong to the requested repository and current operation.
• Before reporting successful remediation, confirm that expected file changes or remote pull-request changes exist.
• Only report “applied” after an application operation succeeds and its result is verified.
• Return a nonzero exit status when no requested operation was performed.
• If the API does not support these operations, rename the commands to reflect their actual account-status behavior.
• Add integration tests that compare repository state before and after fix and remediate.
• Correct SKILL.md, README.md, and SECURITY.md so their claims precisely match implemented behavior.

T08 · Insecure Dependencies

Warning

• Location: scripts/docker-compose.yml:5

Finding

Unpinned Third-Party Container Receives a Bearer Token and Repository Access

Content

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Vulnerability Details

File Location: scripts/docker-compose.yml:5-12
Vulnerability Type: Mutable container dependency with sensitive runtime access
Risk Level: Medium

yaml

services:
  gomboc-mcp:
    image: gombocai/mcp:latest
    container_name: gomboc-mcp-server
    ports:
      - "3100:3100"
    environment:
      - GOMBOC_PAT=${GOMBOC_PAT}
      - MCP_PORT=3100
      - LOG_LEVEL=info
    volumes:
      - ./:/workspace:ro

Technical Analysis

The Compose configuration uses the mutable gombocai/mcp:latest image. A mutable tag does not identify a reproducible, reviewed artifact; its contents can change between deployments without changes to this project.

The downloaded image receives the GOMBOC_PAT bearer token and read-only access to the mounted workspace. Read-only mounting prevents modification through that mount, but it does not prevent the container from reading and transmitting repository contents. The container also has normal bridge-network egress unless the operator applies external restrictions.

Consequently, compromise of the image publisher, registry account, build pipeline, or mutable tag could expose both the authentication token and repository data.

Attack Path

1. The upstream latest tag is changed, compromised, or replaced.
2. A user pulls or starts the Compose service.
3. Docker executes the new image without a project-level digest verification.
4. The container reads GOMBOC_PAT from its environment.
5. The container reads source files from /workspace.
6. The container transmits the token or source through its available network connection.
7. The stolen token is used within its actual server-side scope until it expires or is revoked.

Impact Assessment

A compromised image obtains the Gomboc bearer credential and read access to the mounted project. Its API privileges are limited by the token's actual server-side scope, which cannot be verified from this reposit ...[truncated 196 chars]

Remediation

View remediation

Remediation Suggestions

• Pin the image to a reviewed immutable digest, for example gombocai/mcp@sha256:....
• Establish a documented process for reviewing and intentionally updating the digest.
• Verify image signatures and provenance before deployment.
• Use a dedicated, short-lived token with the smallest available API scope.
• Mount only the specific directory required for processing, not the entire Compose working directory.
• Restrict container egress to explicitly required API destinations.
• Run the container as a fixed non-root user with a read-only root filesystem.
• Drop all Linux capabilities and enable no-new-privileges.
• Use a container secret mechanism instead of a broadly inherited environment variable where supported.
• Rotate the token immediately if an untrusted image version has been executed.

T05 · Unauthorized Access and Privilege Escalation

Warning

• Location: scripts/docker-compose.yml:7

Finding

MCP Service Is Exposed on All Host Interfaces and Configured for Automatic Persistence

Content

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Vulnerability Details

File Location: scripts/docker-compose.yml:7-10 and scripts/docker-compose.yml:19
Vulnerability Type: Excessive network exposure and persistent service configuration
Risk Level: Medium

yaml

container_name: gomboc-mcp-server
ports:
  - "3100:3100"
environment:
  - GOMBOC_PAT=${GOMBOC_PAT}
  - MCP_PORT=3100

yaml

restart: unless-stopped

The documented client requests do not include authentication:

bash

curl -X POST http://localhost:3100/scan \
  -H "Content-Type: application/json" \
  -d '{"path": "./src", "policy": "default"}'

curl -X POST http://localhost:3100/fix \
  -H "Content-Type: application/json" \
  -d '{"path": "./src", "scanId": "scan-id"}'

Technical Analysis

Docker's short port syntax, "3100:3100", normally publishes the service on all host interfaces rather than limiting it to loopback. The documentation describes /scan and /fix requests without a client authentication mechanism.

The external MCP server implementation is not included in the audited project, so the absence of server-side authentication cannot be conclusively verified. Nevertheless, the shipped deployment configuration broadens network reachability beyond the documented local-agent use case.

The restart: unless-stopped policy also causes the service to restart after daemon or host restarts until explicitly stopped. This is not evidence of a malicious backdoor, but it creates cross-session service persistence that is unnecessary for an opt-in local integration by default.

Attack Path

1. A user starts the Compose service on a host reachable by other systems.
2. Docker publishes TCP port 3100 on the host's available interfaces.
3. A remote network user discovers the open port.
4. If the external server lacks independent authentication, the user invokes the documented scan or fix endpoints.
5. The service processes requests while holding the Gomboc token and having read access ...[truncated 613 chars]

Remediation

View remediation

Remediation Suggestions

• Bind the service explicitly to loopback with 127.0.0.1:3100:3100.
• Require authenticated MCP clients even when loopback binding is used.
• Apply request authorization separately from the Gomboc upstream API token.
• Disable automatic restart by default, or use restart: "no" for local interactive use.
• Validate and canonicalize all paths accepted by scan or fix endpoints.
• Ensure requested paths cannot escape the intended workspace.
• Document firewall requirements and warn users before exposing the service remotely.
• Add rate limits and request-size limits.
• Do not expose the service through a public interface without TLS and strong client authentication.

T09 · Insecure Skill Coding Practices

Note

• Location: scripts/cli-wrapper.py:305

Finding

Diagnostic Command Reveals Token Prefix and Setup Guide Recommends Plaintext Secret Persistence

Content

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Vulnerability Details

File Location: scripts/cli-wrapper.py:305-310 and references/setup.md:23-26
Vulnerability Type: Sensitive information exposure and insecure credential storage guidance
Risk Level: Low

python

if args.show_token:
    if GOMBOC_PAT:
        # Never print the actual token
        masked = GOMBOC_PAT[:10] + "***" if len(GOMBOC_PAT) > 10 else "***"
        print(f"✅ GOMBOC_PAT is set (masked: {masked})")

bash

echo 'export GOMBOC_PAT="gpt_your_token_here"' >> ~/.bashrc
source ~/.bashrc

Technical Analysis

For tokens longer than ten characters, the diagnostic command prints the first ten characters. This is partial disclosure of a stable credential and contradicts the project's statement that tokens are never printed.

The setup guide also recommends writing the complete bearer token to .bashrc. Shell startup files are plaintext, may be readable by local tooling, are commonly included in backups, and can be accidentally committed or shared with dotfile repositories.

Partial disclosure alone is not shown to be sufficient to authenticate. However, it reduces secret entropy available to an attacker and can assist correlation of credentials across logs or environments.

Attack Path

1. An operator runs config --show-token in CI, a support session, or a recorded terminal.
2. The first ten token characters are written to logs or terminal history.
3. Alternatively, the operator follows the setup guide and stores the full token in .bashrc.
4. A local process, backup recipient, support bundle, or accidentally published dotfile obtains the disclosed material.
5. If the full token is recovered, it can be used against the Gomboc API until revoked or expired.

Impact Assessment

Full token recovery grants the token's actual Gomboc API permissions. The documentation claims read-only account, run, scan, and fix-event access, but the effective server-side scope cannot be establish ...[truncated 196 chars]

Remediation

View remediation

Remediation Suggestions

• Change --show-token to report only whether a token is configured.
• Never print any token prefix, suffix, length, fingerprint, or other reusable secret material by default.
• Remove the recommendation to append the token to .bashrc.
• Recommend an operating-system credential store, CI secret manager, or dedicated runtime secret injection mechanism.
• Use dedicated tokens per environment and workflow.
• Prefer short-lived and automatically rotated credentials where the service supports them.
• Ensure logs and support bundles redact authorization headers and environment variables.
• Update the security documentation to accurately describe the implemented behavior.

T08 · Insecure Dependencies

Note

• Location: references/github-action.md:23

Finding

GitHub Actions Examples Use Mutable Third-Party Action Tags

Content

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Vulnerability Details

File Location: references/github-action.md:23 and references/github-action.md:86
Vulnerability Type: Unpinned CI supply-chain dependencies
Risk Level: Low

yaml

steps:
  - uses: actions/checkout@v3

  - name: Run Gomboc scan
    env:
      GOMBOC_PAT: ${{ secrets.GOMBOC_PAT }}
    run: |
      python scripts/cli-wrapper.py scan --path ./src --format markdown

yaml

- name: Create Pull Request
  uses: peter-evans/create-pull-request@v5
  with:
    commit-message: "fix: Auto-remediate code issues"
    title: "Gomboc Auto-Remediation"
    branch: gomboc-fixes

Technical Analysis

The examples reference actions using mutable major-version tags. These tags can resolve to different commits over time, so workflow execution is not tied to the exact action code originally reviewed.

GitHub Actions execute within the workflow context and may receive repository permissions or access checked-out content. Although the shown third-party pull-request action is not directly passed GOMBOC_PAT, it can execute in the same job context and may receive a GitHub token or repository permissions depending on workflow configuration.

Attack Path

1. An upstream action repository or release process is compromised, or a mutable tag is moved.
2. A repository uses the documented workflow without pinning the action commit.
3. A later workflow run downloads code resolved by the altered tag.
4. The altered action executes with the workflow's available token, repository contents, and configured permissions.
5. It can tamper with output or abuse permissions granted to the job.

Impact Assessment

The maximum impact depends on the workflow's explicit or default GitHub token permissions. Potential scope includes reading repository contents, modifying branches, or creating pull requests. Access to unrelated secrets is not automatic and depends on event type, job configuration, and whether a secret is supp ...[truncated 54 chars]

Remediation

View remediation

Remediation Suggestions

• Pin every action to a reviewed full commit SHA.
• Record the corresponding release version in a comment for maintainability.
• Use Dependabot or an equivalent controlled process to propose digest updates.
• Add explicit minimal workflow permissions, such as contents: read, unless write access is required.
• Place pull-request creation in a separate job with only the permissions necessary for that operation.
• Avoid exposing sensitive secrets to jobs that execute unnecessary third-party actions.
• Review action provenance and release integrity before updating pinned commits.
• Upgrade deprecated action versions only through reviewed, pinned changes.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep
• Taint Tracking: Direct Taint Flow, Variable-Mediated Taint Flow, Credential Exfiltration Chain

Findings (35)

Tainted flow: 'req' from os.getenv (line 31, credential/environment) → urllib.request.urlopen (network output)

Critical

• Category: Data Flow
• Confidence: 90% confidence

Finding

Credentials or environment variables flow to a network sink. This is a high-confidence indicator of credential exfiltration.

Content

Scanner excerpt · scripts/cli-wrapper.py (reported line 41)May include surrounding context.

python

method="POST"
        )
        
        with urllib.request.urlopen(req, timeout=30) as response:
            result = json.loads(response.read())
            
            if "errors" in result:

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · .clawhub.yml (reported line 27)May include surrounding context.

yaml

GOMBOC_PAT:
    required: true
    sensitive: true
    description: "Gomboc Personal Access Token for API authentication"
    help: "Generate at https://app.gomboc.ai/settings/tokens (free Community Edition account required)"

# Optional environment variables

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · SKILL.md (reported line 19)May include surrounding context.

md

- **Endpoint:** https://api.app.gomboc.ai/graphql
- **Authentication:** Bearer token (Personal Access Token from https://app.gomboc.ai)
- **Required:** Free account at Gomboc.ai (no credit card)
- **Setup:** Generate token in Settings → Personal Access Tokens

**Token Scope & Security (Least Privilege):**
- **Scope:** Read-only API access (minimal required permissions)

Ae1

High

• Category: analysis-evasion
• Confidence: 100% confidence

Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 31)May include surrounding context.

md

- `SKILL.md` — This documentation

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · references/mcp-integration.md (reported line 76)May include surrounding context.

md

- Python 3.7+
- Gomboc account (free at https://app.gomboc.ai)
- Personal Access Token from Gomboc

## 1. Get a Token

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · references/setup.md (reported line 7)May include surrounding context.

md

- Python 3.7+
- Gomboc account (free at https://app.gomboc.ai)
- Personal Access Token from Gomboc

## 1. Get a Token

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · README.md (reported line 32)May include surrounding context.

md

1. Sign up at https://app.gomboc.ai (Community Edition is free)
2. Go to Settings → API Tokens
3. Generate a new Personal Access Token
4. Copy the token (starts with `gpt_`)

## 2. Set Environment Variable

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · SKILL.md (reported line 17)May include surrounding context.

md

1. Sign up at https://app.gomboc.ai (Community Edition is free)
2. Go to Settings → API Tokens
3. Generate a new Personal Access Token
4. Copy the token (starts with `gpt_`)

## 2. Set Environment Variable

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · SKILL.md (reported line 107)May include surrounding context.

md

1. Sign up at https://app.gomboc.ai (Community Edition is free)
2. Go to Settings → API Tokens
3. Generate a new Personal Access Token
4. Copy the token (starts with `gpt_`)

## 2. Set Environment Variable

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · SKILL.md (reported line 270)May include surrounding context.

md

1. Sign up at https://app.gomboc.ai (Community Edition is free)
2. Go to Settings → API Tokens
3. Generate a new Personal Access Token
4. Copy the token (starts with `gpt_`)

## 2. Set Environment Variable

Credential Access

High

• Category: Privilege Escalation
• Confidence: 70% confidence

Finding

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

Content

Scanner excerpt · references/setup.md (reported line 13)May include surrounding context.

md

1. Sign up at https://app.gomboc.ai (Community Edition is free)
2. Go to Settings → API Tokens
3. Generate a new Personal Access Token
4. Copy the token (starts with `gpt_`)

## 2. Set Environment Variable

YARA rule 'backdoor_persistence': Backdoor persistence with malicious payloads (shell commands, SSH key injection, hidden root users) [malware]

High

• Category: YARA Match
• Confidence: 75% confidence

Finding

YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).

Content

Scanner excerpt · references/setup.md (reported line 25)May include surrounding context.

oc account (free at https://app.gomboc.ai)

• Personal Access Token from Gomboc

1. Get a Token

1. Sign up at https://app.gomboc.ai (Community Edition is free)
2. Go to Settings → API Tokens
3. Generate a new Personal Access Token
4. Copy the token (starts with gpt_)

2. Set Environment Variable

bash

export GOMBOC_PAT="gpt_your_token_here"

Or add to your shell profile:

bash

echo 'export GOMBOC_PAT="gpt_your_token_here"' >> ~/.bashrc
source ~/.bashrc

3. Verify Setup

bash

bash scripts/verify-setup.sh

Should output:

text

✅ GOMBOC_PAT is set
✅ Python 3 found: Python 3.7+
✅ CLI wrapper found
✅ API connection successful
✅ Setup verification complete!

4. Run Your First Scan

bash

python scripts/cli-wrapper.py scan --path ./src

Troubleshooting

"GOMBOC_PAT not set"

bash

export GOMBOC_PAT="your_token"

"API connection failed"

• Check your token is correct
• Check you can reach https://api.app.gomboc.ai
• Check yo

Intent-Code Divergence

High

• Category: Not specified by scanner
• Confidence: 98% confidence

Finding

The docstring, CLI description, and flags like --commit and --push imply code changes and repository actions. However, the implementation merely queries recent fixEvents, prints counts, and never edits files, commits changes, or pushes to a remote.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

This markdown file states that the remediate command 'Applies fixes directly to codebase,' which is a file-modifying operation. Under the markdown criteria for missing user warnings, the description does not warn about potential source changes, review needs, or user data/system integrity impact.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The README advertises automatic pull request creation and continuous code remediation, but it does not prominently warn that these actions can modify repositories and create commits. In an agent skill context, that omission can cause users or downstream agents to invoke repository-changing behavior without adequate review, increasing the risk of unintended code changes or workflow abuse.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 97% confidence

Finding

The example command remediate --commit demonstrates a repository-modifying operation without an adjacent warning that it will write changes and create commits. In an agent-driven environment, copy-pasted or automated execution of this command could lead to unreviewed commits, accidental branch pollution, or unauthorized changes if run with elevated repository permissions.

Content

No source excerpt is available for this finding.

Rp1

Medium

• Category: MCP Rug Pull
• Confidence: 95% confidence

Finding

The documentation references a Docker image by name without a pinned tag or digest, which makes executions non-reproducible and exposes users to supply-chain risk if the upstream image changes unexpectedly. Even though this is only an example command, users may copy and run it directly, pulling an unintended or compromised image.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The skill documents direct remediation and commit operations without clearly warning that these commands modify local code and can create commits automatically. In an agent or CI context, this can lead to unintended repository changes, integrity issues, or unsafe automated edits being applied without review.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The examples promote remediate with commit:true and push:true without cautioning that these actions can automatically alter and publish repository state. In unattended use, this increases the chance of destructive or policy-violating changes being committed and pushed without adequate review.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The agent workflow encourages autonomous scan-fix-apply loops and describes them as safe for agents, but does not include safeguards against unattended code modification. This is riskier in agentic contexts because agents may execute workflows at scale across many repositories or branches.

Content

No source excerpt is available for this finding.

Rp1

Medium

• Category: MCP Rug Pull
• Confidence: 98% confidence

Finding

The docker run example uses gombocai/mcp:latest, which is an unpinned mutable tag and can silently change over time. This creates a supply-chain and reproducibility risk, especially because the container is presented as an agent-facing integration point and may handle sensitive source code or tokens.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The MCP examples include remediation with commit enabled but omit a warning that the command changes local code state and creates commits. Since MCP is intended for agent integration, the omission is more dangerous because users may treat these examples as safe default automation behavior.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The GitHub Actions example enables auto-fix: true without warning that it may automatically modify repository contents. In CI/CD, automated modifications can have broad impact because they may run on many branches or pull requests and operate with repository-scoped credentials.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 98% confidence

Finding

The file presents directly copyable Terraform that includes insecure configurations such as an unencrypted S3 bucket, overly permissive security group rules, a hardcoded database password, public database exposure, long-lived IAM access keys, and a KMS key without rotation. Although the examples are labeled with inline 'Issue' comments, there is no prominent warning at the start that applying this code will create intentionally insecure infrastructure, so users may copy it into production or test environments and expose data, credentials, and cloud resources.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

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

Finding

The documented workflow performs automatic code modification and pushes changes back to the repository using --commit --push without a prominent warning about repository mutation, review implications, or branch-targeting safety. In CI/CD contexts this can lead to unintended commits, workflow loops, or unsafe changes being applied automatically if users copy the example as-is.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.