PraisonAI Vulnerable to Argument Injection into Cloud Run Environment Variables via Unsanitized Comma in gcloud --set-env-vars

Step 3. View the full context of the vulnerable construction

    sed -n '140,165p' \
      src/praisonai/praisonai/deploy.py

This block shows the gcloud command list where the three values are
joined into one comma-separated string passed as a single argument
element. gcloud receives this string and applies its own
comma-based parsing, which the subprocess list form cannot prevent.

Step 4. Confirm subprocess is called without shell=True

    grep -n "subprocess\|Popen\|shell=" \
      src/praisonai/praisonai/deploy.py

This confirms shell=False (default), meaning the injection is at the
gcloud argument level, not the shell level. The comma delimiter is
parsed by gcloud itself, not by /bin/sh.

Step 5. Confirm no existing advisory covers this file

    grep -rn "deploy.py\|set.env.vars\|openai_base" \
      src/praisonai/praisonai/deploy.py

Vulnerability Description

File: src/praisonai/praisonai/deploy.py

Vulnerable line:

  150: '--set-env-vars', f'OPENAI_MODEL_NAME={openai_model},OPENAI_API_KEY={openai_key},OPENAI_API_BASE={openai_base}'

The three values openai_model, openai_key, and openai_base originate from environment variables or user-provided configuration and are interpolated directly into a single f-string without validation.

The subprocess call uses a Python list without shell=True. This means there is no shell injection. The subprocess module passes the f-string as one complete argument to gcloud. gcloud then applies its own internal parsing to the value of --set-env-vars using a comma as the delimiter. This parsing is entirely outside Python's control.

If any of the three values contains a comma, gcloud splits on that comma and creates an additional KEY=VALUE environment variable from the text following it. There is no error or warning from gcloud when this occurs.

The three values are attacker-controllable in any scenario where environment variables can be set before the deploy command runs. This includes compromised dotenv files, poisoned CI pipeline secrets, and local developer machines where an attacker has shell access.

Proof of Concept

 attacker-controlled openai_base value:

    export OPENAI_API_KEY="sk-legitimate-key"
    export OPENAI_MODEL_NAME="gpt-4"
    export OPENAI_API_BASE="https://api.openai.com/v1,INJECTED=attacker_value"

Run the deploy command. The string constructed at line 150 becomes:

    OPENAI_MODEL_NAME=gpt-4,OPENAI_API_KEY=sk-legitimate-key,OPENAI_API_BASE=https://api.openai.com/v1,INJECTED=attacker_value

gcloud parses this as four key-value pairs and creates all four as environment variables in the Cloud Run service. INJECTED=attacker_value is a real environment variable available to every request the service handles.

Verify the injection after deployment:

    gcloud run services describe praisonai-service \
      --region us-central1 \
      --format "value(spec.template.spec.containers[0].env)"

The output includes INJECTED alongside the three legitimate variables.

API key override:

export OPENAI_API_KEY="sk-real,OPENAI_API_KEY=sk-attacker"

The constructed string contains OPENAI_API_KEY twice. In gcloud versions where the last-defined value takes precedence, the deployed service uses sk-attacker for all LLM API calls. All agent traffic routes through the attacker-controlled API account.

Impact

An attacker who can influence any of the three environment variables before deploy.py runs can inject arbitrary environment variables into the deployed Cloud Run production service without triggering any error.

Injection scenarios include a malicious git hook that modifies a dotenv file before deployment, a compromised CI pipeline secret, or any local access that allows setting environment variables in the deploy shell session.

Consequences include overriding the API key used by the production service, injecting proxy settings that redirect all outbound LLM traffic, setting debug or verbose flags that write sensitive data to Cloud Run logs, and overriding any security-relevant variable the service reads from its environment.

The API key override scenario is the highest-impact case. All production LLM calls made by the deployed service are billed to and logged by the attacker's API account, giving the attacker full visibility into every agent prompt and response processed in production.

Recommended Fix

Pass each variable as a separate --update-env-vars flag so each value is an isolated argument and gcloud never performs comma-based parsing across multiple values:

Before:
  ['gcloud', 'run', 'deploy', 'praisonai-service',
   '--set-env-vars',
   f'OPENAI_MODEL_NAME={openai_model},OPENAI_API_KEY={openai_key},OPENAI_API_BASE={openai_base}']

After:
  ['gcloud', 'run', 'deploy', 'praisonai-service',
   '--update-env-vars', f'OPENAI_MODEL_NAME={openai_model}',
   '--update-env-vars', f'OPENAI_API_KEY={openai_key}',
   '--update-env-vars', f'OPENAI_API_BASE={openai_base}']

Each --update-env-vars element is a separate string in the subprocess list. The subprocess module passes each as a distinct argument to gcloud. gcloud receives three separate single-variable assignments and performs no cross-argument comma parsing.

Add pre-flight validation as a secondary control:

for label, value in [
    ("OPENAI_MODEL_NAME", openai_model),
    ("OPENAI_API_KEY", openai_key),
    ("OPENAI_API_BASE", openai_base),
]:
    if "," in value:
        raise ValueError(
            f"{label} contains a comma and would corrupt "
            f"--set-env-vars: {value!r}"
        )

References

CWE-88 Improper Neutralization of Argument Delimiters in a Command gcloud run deploy documentation for --set-env-vars KEY=VALUE comma delimiter specification