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GHSA-g6j7-pffp-8whg CVE-2026-61444 CVE-2026-62176 PYSEC-2026-4256

PraisonAI: Code Injection via f-string Interpolation in Deploy API Server Generation

Critical PyPI
Severity
9.4 / 10
Critical · CVSS 4.0
Exploit chance (EPSS)
0.50%
Chance of exploitation in the next 30 days. Higher than 42.56% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix
4.6.78
Upgrade praisonai to this version or later

Description

Summary

The deploy/api.py module generates Python server code by directly interpolating the agents_file parameter into an f-string that is then written to a file and executed via subprocess.Popen(). An attacker who controls the agents_file value (via CLI argument, configuration, or upstream API) can inject arbitrary Python code.

Details

src/praisonai/praisonai/deploy/api.py (line 80):

code = f'''...
    praisonai = PraisonAI(agent_file="{agents_file}")
...
    "agent_file": "{agents_file}"
...'''

The generated code is then executed (line 190):

subprocess.Popen(['python', server_file])

agents_file is never sanitized or validated. A malicious value breaks out of the string context:

agents_file = '"); import os; os.system("id"); #'
# Generated code becomes:
# praisonai = PraisonAI(agent_file=""); import os; os.system("id"); #")

The same pattern exists in deploy/docker.py (line 33) for Dockerfile generation.

PoC

# The injection:
agents_file = '"); import os; os.system("id"); #'

# What the generated code looks like:
template = f'praisonai = PraisonAI(agent_file="{agents_file}")'
print(template)
# Output: praisonai = PraisonAI(agent_file=""); import os; os.system("id"); #")

Impact

  • Arbitrary code execution on the machine running the deploy command
  • Supply chain risk if agents_file comes from a configuration file or CI/CD pipeline

Affected versions

PyPI

PackageVulnerableFixed in
praisonai
< 4.6.78
4.6.78

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 → 4.6.78

pip
pip install --upgrade "praisonai>=4.6.78"
poetry
poetry add "praisonai>=4.6.78"
uv
uv add "praisonai>=4.6.78"

Severity breakdown

CVSS 4.0 9.4

CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Attack vector
Network
Attack complexity
Low
Attack requirements
None
Privileges required
High
User interaction
None
Confidentiality (vulnerable system)
High
Integrity (vulnerable system)
High
Availability (vulnerable system)
High
Confidentiality (subsequent systems)
High
Integrity (subsequent systems)
High
Availability (subsequent systems)
High
Exploit maturity
Not defined
Scope
X

CVSS 3.1 9.1

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

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

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it
  • Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
  • Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
  • 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. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
  • 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.
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-26mh-57q7-jfvr
PraisonAI vulnerable to Remote Code Execution via Broken AST Sandbox
High Also affects praisonai
GHSA-6g6r-q6gw-w8fg
PraisonAI has a Browser Server WebSocket origin validation bypass via unanchored regex (patch bypass of CVE-2026-40289 / GHSA-8x8f-54wf-vv92)
Critical Also affects praisonai
GHSA-pvph-5j39-v8qc
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High Also affects praisonai
GHSA-gfq8-hmph-9gjv
PraisonAI: Authentication fail-open in Recipe server allows unauthenticated access when API key or JWT auth is configured without a secret
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GHSA-2jgc-f764-c5r2
PraisonAI: [Auth Bypass] PraisonAI async Jobs API (`/api/v1/runs`) has no authentication — unauthenticated job execution, result theft, cancel and delete
High Also affects praisonai
GHSA-pvxx-r596-f5qj
PraisonAI: `--api-key` flag on `praisonai serve` is not properly enforced
High Also affects praisonai
GHSA-hmfx-4v44-9qw9
PraisonAI vulnerable to Server-Side Request Forgery via DNS rebinding bypass in webhook_url validation
Medium Also affects praisonai
GHSA-rg5q-pp8p-f7jm
PraisonAI: Webhook SSRF via DNS fail-open in `JobSubmitRequest.validate_webhook_url()` — bypass of CVE-2026-40114
High Also affects praisonai
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