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GHSA-fvxx-ggmx-3cjg CVE-2026-40113 PYSEC-2026-2913

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

High PyPI
Severity
8.4 / 10
High · CVSS 3.1
Exploit chance (EPSS)
0.23%
Chance of exploitation in the next 30 days. Higher than 13.98% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-88 Improper Neutralization of Argument Delimiters in a Command ('Argument Injection')
Fix
4.5.128
Upgrade praisonai to this version or later

Description

Summary

deploy.py constructs a single comma-delimited string for the gcloud run deploy --set-env-vars argument by directly interpolating openai_model, openai_key, and openai_base without validating that these values do not contain commas. gcloud uses a comma as the key-value pair separator for --set-env-vars. A comma in any of the three values causes gcloud to parse the trailing text as additional KEY=VALUE definitions, injecting arbitrary environment variables into the deployed Cloud Run service.

Grep Commands and Evidence

Step 1. Confirm the vulnerable string construction at line 150

    grep -n "set-env-vars\|openai_key\|openai_base\|openai_model" \
      src/praisonai/praisonai/deploy.py
Expected output showing unsanitized interpolation:
150:  '--set-env-vars', f'OPENAI_MODEL_NAME={openai_model},OPENAI_API_KEY={openai_key},OPENAI_API_BASE={openai_base}'

Step 2. Confirm no comma validation exists before this line

    grep -n "comma\|assertNotIn\|ValueError\|sanitize\|strip\|replace" \
      src/praisonai/praisonai/deploy.py
Expected output: no results related to input validation

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

Affected versions

PyPI

PackageVulnerableFixed in
praisonai
< 4.5.128
4.5.128

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

praisonai PyPI → 4.5.128

pip
pip install --upgrade "praisonai>=4.5.128"
poetry
poetry add "praisonai>=4.5.128"
uv
uv add "praisonai>=4.5.128"

Severity breakdown

CVSS 3.1 8.4

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:N

Attack vector
Local
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Changed
Confidentiality
High
Integrity
High
Availability
None

Weakness

CWE-88: Improper Neutralization of Argument Delimiters in a Command ('Argument Injection')

The product constructs a string for a command to be executed by a separate component in another control sphere, but it does not properly delimit the intended arguments, options, or switches within that command string.

How to prevent it
  • Implementation: Where possible, avoid building a single string that contains the command and its arguments. Some languages or frameworks have functions that support specifying independent arguments, e.g. as an array, which is used to automatically perform the appropriate quoting or escaping while building the command. For example, in PHP, escapeshellarg() can be used to escape a single argument to system(), or exec() can be called with an array of arguments. In C, code can often be refactored from using system() - which accepts a single string - to using exec(), which requires separate function arguments for each parameter.
  • Architecture and Design: Understand all the potential areas where untrusted inputs can enter your product: parameters or arguments, cookies, anything read from the network, environment variables, request headers as well as content, URL components, e-mail, files, databases, and any external systems that provide data to the application. Perform input validation at well-defined interfaces.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
  • Implementation: Directly convert your input type into the expected data type, such as using a conversion function that translates a string into a number. After converting to the expected data type, ensure that the input's values fall within the expected range of allowable values and that multi-field consistencies are maintained.
  • Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
  • Implementation: When exchanging data between components, ensure that both components are using the same character encoding. Ensure that the proper encoding is applied at each interface. Explicitly set the encoding you are using whenever the protocol allows you to do so.
  • Implementation: When your application combines data from multiple sources, perform the validation after the sources have been combined. The individual data elements may pass the validation step but violate the intended restrictions after they have been combined.
  • Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
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