Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-33710 is a high-severity Use of Insufficiently Random Values (CWE-330) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Forge Web Credentials (T1606); ranked at the 21th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SC-12 (Cryptographic Key Establishment and Management) and SC-13 (Cryptographic Protection) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-33710 is a vulnerability in Chamilo LMS, an open-source learning management system, affecting versions prior to 1.11.38 and 2.0.0-RC.3. The issue stems from flawed REST API key generation, where keys are produced using the MD5 hash of time() + (user_id * 5) - rand(10000, 10000). The rand() function with identical min and max parameters always returns exactly 10000, reducing the formula to a predictable md5(timestamp + user_id*5 - 10000). This weakness aligns with CWE-330: Use of Insufficiently Random Values and carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N).
An unauthenticated attacker with network access can exploit this vulnerability if they know a target username and the approximate key creation timestamp. By brute-forcing a narrow range of timestamps combined with the known user_id, they can compute the exact API key, enabling unauthorized access to REST API endpoints and potentially exposing sensitive learning management data.
The vulnerability is addressed in Chamilo LMS versions 1.11.38 and 2.0.0-RC.3. Patches are detailed in GitHub commits 4448701bb8ec557e94ef02d19c72cbe9c49c2d09 and e7400dd840586ae134b286d0a2374f3d269a9a9d, with further guidance in the security advisory at GHSA-rpmg-j327-mr39. Administrators should upgrade to a fixed version to mitigate the risk.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-21565
Vulnerability Data
Chamilo LMS is a learning management system. Prior to 1.11.38 and 2.0.0-RC.3, REST API keys are generated using md5(time() + (user_id * 5) - rand(10000, 10000)). The rand(10000, 10000) call always returns exactly 10000 (min == max), making the formula…
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effectively md5(timestamp + user_id*5 - 10000). An attacker who knows a username and approximate key creation time can brute-force the API key. This vulnerability is fixed in 1.11.38 and 2.0.0-RC.3.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 8 hardening rules · 4 OS baselines
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Mitigating Controls (NIST 800-53 r5) AI
SC-12 requires proper cryptographic key establishment and management, which structurally mandates use of sufficient randomness for key generation.
SC-13 requires selection and implementation of approved cryptographic algorithms and methods, which inherently depend on and enforce sufficiently random values.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly enforce use of cryptographically strong RNGs and catch insufficient randomness during design, coding, and testing.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Cryptographic controls require use of approved, sufficiently random values for keys and nonces.
Security testing can detect weak randomness but does not prescribe the control itself.
Secure SDLC processes include verification steps that can catch insufficient randomness but do not directly specify RNG requirements.
Secure coding standards explicitly prohibit use of weak or predictable random number generators.
Secure authentication mechanisms depend on unpredictable values (nonces, salts, session tokens) to resist guessing.
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (authoritative rows only; rows rated `none` are excluded).
Oracle Linux 8 (3 rules)
- V-248563 The OL 8 SSH server must be configured to use strong entropy. prevents CWE-330
- V-248599 OL 8 must enable the hardware random number generator entropy gatherer service. prevents CWE-330
- V-248600 OL 8 must have the packages required to use the hardware random number generator entropy gatherer service. prevents CWE-330
Oracle Linux 9 (1 rule)
- V-271511 OL 9 must enable the hardware random number generator entropy gatherer service. prevents CWE-330
RHEL 8 (3 rules)
- V-244527 RHEL 8 must have the packages required to use the hardware random number generator entropy gatherer service. prevents CWE-330
- V-230253 RHEL 8 must ensure the SSH server uses strong entropy. prevents CWE-330
- V-230285 RHEL 8 must enable the hardware random number generator entropy gatherer service. prevents CWE-330
RHEL 9 (1 rule)
- V-257782 RHEL 9 must enable the hardware random number generator entropy gatherer service. prevents CWE-330