Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:LSummary
CVE-2026-44978 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Neutrinolabs Xrdp. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 24th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-46030
Vulnerability Data
xrdp is an open source RDP server. Versions 0.10.6 and prior contain a heap out-of-bounds read vulnerability within the FIPS-specific receive paths. This vulnerability does not affect the default configuration of xrdp. The vulnerability is only exploitable when the security…
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layer is set to security_layer=negotiate or security_layer=rdp, and the crypto level is changed to crypt_level=fips in xrdp.ini. In this specific non-default mode, the server fails to validate the FIPS padding length field, leading to a pointer underflow and a subsequent negative length calculation. An unauthenticated remote attacker can exploit this by sending a crafted FIPS-protected PDU, causing a heap out-of-bounds read that results in a process crash and denial of service (DoS). However, since xrdp forks a new process for each connection by default, an out-of-bounds read causing a process crash is unlikely to bring down the entire xrdp service. This issue has been fixed in version 0.10.6.1.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Vulnerability in public-facing xrdp RDP server allows unauthenticated remote exploitation via crafted PDU in non-default config, directly enabling T1190.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 6 hardening rules · 3 OS baselines
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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.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.
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 require and enforce input validation during development.
Vulnerability scanning and recording can discover instances of out-of-bounds reads after code is deployed.
Routine patching replaces vulnerable code containing out-of-bounds read flaws.
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.
Testing against a defined set of requirements and using code review plus vulnerability scanning forces validation of inputs and handling of unanticipated conditions, reducing the chance that malformed data will be accepted.
Logging can record evidence of an out-of-bounds read but does not prevent the weakness itself.
Secure-coding guidelines and mandatory security testing (including code scans) compel developers to validate and sanitize inputs at design and implementation time, lowering the incidence of malformed or malicious data reaching downstream components.
Mandating input controls that include integrity checks and input validation ensures that untrusted data is examined before use, blocking the root cause of many injection and malformed-data weaknesses.
Security-by-design principles explicitly call for data validation and sanitization at every layer, reducing the chance that malformed or malicious input will be processed without scrutiny.
Requiring language-specific secure coding standards, peer review, SAST and documented mitigation of common programming errors forces validation of all inputs before they are trusted.