Vulnerabilidades de MervinPraison

PraisonAI is a multi-agent teams system. Prior to version 4.6.34, PraisonAI's MCP (Model Context Protocol) server (praisonai mcp serve) registers four file-handling tools by default — praisonai.rules.create, praisonai.rules.show, praisonai.rules.delete, and praisonai.workflow.show. Each accepts a path or filename string from MCP tools/call arguments and joins it onto ~/.praison/rules/ (or, for workflow.show, accepts an absolute path) with no containment check. The JSON-RPC dispatcher passes params["arguments"] blind to each handler via **kwargs without validating against the advertised input schema. By setting rule_name="../../<some-path>" an attacker walks out of the rules directory and writes any file the running user can write. Dropping a Python .pth file into the user site-packages directory escalates this primitive to arbitrary code execution in any subsequent Python process the user spawns — the next praisonai CLI invocation, an IDE script run, the user's python REPL, or any background Python service. This issue has been patched in version 4.6.34.

PraisonAI is a multi-agent teams system. Prior to version 1.6.32, the URL checking logic in PraisonAI has a logical flaw that could be bypassed by attackers, leading to SSRF attacks. This issue has been patched in version 1.6.32.

PraisonAI is a multi-agent teams system. Prior to version 4.6.9, the fix for PraisonAI's MCP command handling does not add a command allowlist or argument validation to parse_mcp_command(), allowing arbitrary executables like bash, python, or /bin/sh with inline code execution flags to pass through to subprocess execution. This issue has been patched in version 4.6.9.

PraisonAI is a multi-agent teams system. Prior to 4.5.133, there is an SQL identifier injection vulnerability in SQLiteConversationStore where the table_prefix configuration value is directly concatenated into SQL queries via f-strings without any validation or sanitization. Since SQL identifiers cannot be safely parameterized, an attacker who controls the table_prefix value (e.g., through from_yaml or from_dict configuration input) can inject arbitrary SQL fragments that alter query structure. This enables unauthorized data access, such as reading internal SQLite tables like sqlite_master, and manipulation of query results through techniques like UNION-based injection. The vulnerability propagates from configuration input in config.py, through factory.py, to the SQL query construction in sqlite.py. Exploitation requires the ability to influence configuration input, and successful exploitation leads to internal schema disclosure and full query result tampering. This issue has been fixed in version 4.5.133.

PraisonAI is a multi-agent teams system. In versions 4.5.139 and below, the GitHub Actions workflows are vulnerable to ArtiPACKED attack, a known credential leakage vector caused by using actions/checkout without setting persist-credentials: false. By default, actions/checkout writes the GITHUB_TOKEN (and sometimes ACTIONS_RUNTIME_TOKEN) into the .git/config file for persistence, and if any subsequent workflow step uploads artifacts (build outputs, logs, test results, etc.), these tokens can be inadvertently included. Since PraisonAI is a public repository, any user with read access can download these artifacts and extract the leaked tokens, potentially enabling an attacker to push malicious code, poison releases and PyPI/Docker packages, steal repository secrets, and execute a full supply chain compromise affecting all downstream users. The issue spans numerous workflow and action files across .github/workflows/ and .github/actions/. This issue has been fixed in version 4.5.140.

PraisonAI is a multi-agent teams system. In versions below 4.5.139 of PraisonAI and 1.5.140 of praisonaiagents, the browser bridge (praisonai browser start) is vulnerable to unauthenticated remote session hijacking due to missing authentication and a bypassable origin check on its /ws WebSocket endpoint. The server binds to 0.0.0.0 by default and only validates the Origin header when one is present, meaning any non-browser client that omits the header is accepted without restriction. An unauthenticated network attacker can connect, send a start_session message, and the server will route it to the first idle browser-extension WebSocket (effectively hijacking that session) and then broadcast all resulting automation actions and outputs back to the attacker. This enables unauthorized remote control of connected browser automation sessions, leakage of sensitive page context and automation results, and misuse of model-backed browser actions in any environment where the bridge is network-reachable. This issue has been fixed in versions 4.5.139 of PraisonAI and 1.5.140 of praisonaiagents.

PraisonAI is a multi-agent teams system. In versions below 4.5.139 of PraisonAI and 1.5.140 of praisonaiagents, the workflow engine is vulnerable to arbitrary command and code execution through untrusted YAML files. When praisonai workflow run <file.yaml> loads a YAML file with type: job, the JobWorkflowExecutor in job_workflow.py processes steps that support run: (shell commands via subprocess.run()), script: (inline Python via exec()), and python: (arbitrary Python script execution)—all without any validation, sandboxing, or user confirmation. The affected code paths include action_run() in workflow.py and _exec_shell(), _exec_inline_python(), and _exec_python_script() in job_workflow.py. An attacker who can supply or influence a workflow YAML file (particularly in CI pipelines, shared repositories, or multi-tenant deployment environments) can achieve full arbitrary command execution on the host system, compromising the machine and any accessible data or credentials. This issue has been fixed in versions 4.5.139 of PraisonAI and 1.5.140 of praisonaiagents.