Password prediction via non-cryptographic random sampling
Published Dec 6, 2022 · Updated Apr 23, 2025
Weak random generation in Arjun Sharda Passeo before 1.0.5 allows attackers to predict passwords created by the package. The generate method builds a character pool and passes it to Python's non-cryptographic random.sample function, exposing generated values to PRNG prediction. Exploitation requires a password from an affected release to protect an account; successful prediction discloses the secret used for authentication.
Summary
What happened
Weak random generation in Arjun Sharda Passeo before 1.0.5 allows attackers to predict passwords created by the package. The generate method builds a character pool and passes it to Python's non-cryptographic random.sample function, exposing generated values to PRNG prediction. Exploitation requires a password from an affected release to protect an account; successful prediction discloses the secret used for authentication.
The record
- CVE
- CVE-2022-23472
- Published
- Dec 6, 2022
- Updated
- Apr 23, 2025
- Vendor
- Arjun Sharda
- Product
- Passeo
- Classifications
- CWE-338, T1110.001
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Dec 6, 2022CVE publishedPublication date reported by the CVE source.
- Apr 23, 2025Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=< 1.0.5
What conditions does exploitation require?
What is affected?
Published CVSS scores
CVSS describes severity. EPSS estimates exploitation probability.
Attacks
What attackers are doing with it
Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.
Weakness, pattern, technique
Public exploit references
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Technologies
Your stack
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Your stack
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