Scylla is a real-time big data database that is API-compatible with Apache Cassandra and Amazon DynamoDB. When decompressing CQL frame received from user, Scylla assumes that user-provided uncompressed length is correct. If user provides fake length, that is greater than the real one, part of decompression buffer won't be overwritten, and will be left uninitialized. This can be exploited in several ways, depending on the privileges of the user. 1. The main exploit is that an attacker with access to CQL port, but no user account, can bypass authentication, but only if there are other legitimate clients making connections to the cluster, and they use LZ4. 2. Attacker that already has a user account on the cluster can read parts of uninitialized memory, which can contain things like passwords of other users or fragments of other queries / results, which leads to authorization bypass and sensitive information disclosure. The bug has been patched in the following versions: Scylla Enterprise: 2020.1.14, 2021.1.12, 2022.1.0. Scylla Open Source: 4.6.7, 5.0.3. Users unable to upgrade should make sure none of their drivers connect to cluster using LZ4 compression, and that Scylla CQL port is behind firewall. Additionally make sure no untrusted client can connect to Scylla, by setting up authentication and applying workarounds from previous point (firewall, no lz4 compression).
CVE-2022-29240
This high-severity CVE scores 8.1 under NVD CVSS v3. EPSS exploit probability: 1.0%, top 40% of all CVEs by exploit prediction. GitHub Security Advisory data not yet ingested — confidence will rise once GHSA publishes (typical lag: hours to days for open-source ecosystem CVEs; never for infrastructure-only CVEs).
- High severity, but no confirmed exploitation yet
No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.
- CVSS v3
- 8.1
- EG Score
- 8.1(medium)
- EG Risk
- 37(Track)EG Risk 37/100SSVC: Track
EG Risk is EchelonGraph's 0–100 priority score: it fuses intrinsic severity with real-world exploitation and automatability so you can rank equal-severity CVEs and fix the most dangerous first. Higher = act sooner. Distinct from the 0–10 EG Score (severity).
How it’s computedSeverity81% × 45%Exploitation1% × 40%Automatability0% × 15%Action: Routine — remediate on your standard cadence. - EPSS
- 59.7%
- KEV
- Not listed
Published
September 15, 2022
Last Modified
November 21, 2024
References (6)
- security-advisories@githubhttps://github.com/scylladb/scylla/security/advisories/GHSA-25pq-rrqm-6fmr
- security-advisories@githubhttps://github.com/scylladb/scylladb/commit/1c2eef384da439b0457b6d71c7e37d7268e471cb
- security-advisories@githubhttps://github.com/scylladb/scylladb/issues/11476
- af854a3a-2127-422b-91ae-364da2661108https://github.com/scylladb/scylla/security/advisories/GHSA-25pq-rrqm-6fmr
- af854a3a-2127-422b-91ae-364da2661108https://github.com/scylladb/scylladb/commit/1c2eef384da439b0457b6d71c7e37d7268e471cb
- af854a3a-2127-422b-91ae-364da2661108https://github.com/scylladb/scylladb/issues/11476
Weakness Classification(1)
MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.
Data Freshness Timeline
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Frequently asked(5)
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Dependency Blast Radius
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