CVE-2020-7385
Published: 23 April 2021
Summary
CVE-2020-7385 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Rapid7 Metasploit. Its CVSS base score is 8.1 (High).
Operationally, ranked in the top 30.6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-5787
Vulnerability details
By launching the drb_remote_codeexec exploit, a Metasploit Framework user will inadvertently expose Metasploit to the same deserialization issue that is exploited by that module, due to the reliance on the vulnerable Distributed Ruby class functions. Since Metasploit Framework typically runs…
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with elevated privileges, this can lead to a system compromise on the Metasploit workstation. Note that an attacker would have to lie in wait and entice the Metasploit user to run the affected module against a malicious endpoint in a "hack-back" type of attack. Metasploit is only vulnerable when the drb_remote_codeexec module is running. In most cases, this cannot happen automatically.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.