Skip to content

Loading malicious session files can lead to Code Execution

Moderate
guedou published GHSA-cq46-m9x9-j8w2 Oct 22, 2025

Package

pip scapy (pip)

Affected versions

2.0.0

Patched versions

2.7.0

Description

Summary

An unsafe deserialization vulnerability in Scapy <v2.7.0 allows attackers to execute arbitrary code when a malicious session file is locally loaded via the -s option. This requires convincing a user to manually load a malicious session file.

Details

Scapy’s interactive shell supports session loading using gzip-compressed pickle files:

./run_scapy -s <session_file.pkl.gz>

Internally, this triggers:

# main.py
SESSION = pickle.load(gzip.open(session_name, "rb"))

Since no validation or restriction is performed on the deserialized object, any code embedded via __reduce__() will be executed immediately. This makes it trivial for an attacker to drop a malicious .pkl.gz in a shared folder and have it executed by unsuspecting users.

The vulnerability exists in the load_session function, which deserializes data using pickle.load() on .pkl.gz files provided via the -s CLI flag or programmatically through conf.session.

Affected lines in source code:
https://github.com/secdev/scapy/blob/master/scapy/main.py#L569-L572

try:
    s = pickle.load(gzip.open(fname, "rb"))
except IOError:
    try:
        s = pickle.load(open(fname, "rb"))

Impact

This is a classic deserialization vulnerability which leads to Code Execution (CE) when untrusted data is deserialized.

Any user who can trick another user into loading a crafted .pkl.gz session file (e.g. via -s option) can execute arbitrary Python code.

  • Vulnerability type: Insecure deserialization (Python pickle)
  • CWE: CWE-502: Deserialization of Untrusted Data
  • CVSS v4.0 Vector: CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
  • CVSS Score: 5.4 (Medium)
  • Impact: Arbitrary Code Execution
  • Attack vector: Local or supply chain (malicious .pkl.gz)
  • Affected users: Any user who loads session files (even interactively)
  • Affected version: Scapy v2.6.1

Mitigations

  • Do not use 'sessions' (the -s option when launching Scapy).
  • Use the Scapy 2.7.0+ where the session mechanism has been removed.

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Local
Attack Complexity Low
Attack Requirements Present
Privileges Required Low
User interaction Active
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

CVE ID

No known CVE

Weaknesses

Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Learn more on MITRE.

Credits