CVE-2016-20030
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2016-20030 is a critical-severity Incorrect Behavior Order: Authorization Before Parsing and Canonicalization (CWE-551) vulnerability in Ibmcloud (inferred from references). Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Password Guessing (T1110.001); ranked at the 44th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to IA-6 (Authentication Feedback) and SI-11 (Error Handling) — 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.
ZKTeco ZKBioSecurity 3.0 suffers from a user enumeration vulnerability, identified as CVE-2016-20030 and associated with CWE-551. This flaw enables unauthenticated attackers to identify valid usernames by submitting partial character strings through the username parameter in requests to the authLoginAction!login.do script. The application responds differently to valid versus invalid inputs, allowing attackers to systematically discover active user accounts. The vulnerability has a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H), reflecting its critical severity due to network accessibility and lack of prerequisites.
Unauthenticated remote attackers can exploit this vulnerability over the network with low complexity and no user interaction required. By crafting and sending multiple HTTP requests with incremental or partial username guesses to the login endpoint, attackers differentiate valid accounts based on response variations, such as timing, status codes, or error messages. Successful enumeration provides a list of legitimate usernames, which can facilitate subsequent attacks like targeted brute-force password guessing, credential stuffing, or social engineering.
Advisories from sources including IBM X-Force Exchange, Packet Storm Security, VulnCheck, and Zero Science (ZSL-2016-5366) document the issue and provide technical details on exploitation, though specific patch information or mitigation steps are referenced in those publications for further review by practitioners.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2016-10815
Vulnerability Data
ZKTeco ZKBioSecurity 3.0 contains a user enumeration vulnerability that allows unauthenticated attackers to discover valid usernames by submitting partial characters via the username parameter. Attackers can send requests to the authLoginAction!login.do script with varying username inputs to enumerate valid user…
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accounts based on application responses.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
User enumeration directly supports valid account discovery for subsequent brute-force guessing and credential stuffing attacks.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
IA-6 obscures authentication feedback and prevents disclosure of usernames during failed attempts, directly eliminating the differing responses that enable user enumeration via partial inputs.
SI-11 restricts error messages to minimal, non-revealing content, preventing the application from leaking information about valid usernames through response variations.
AC-7 enforces limits on unsuccessful logon attempts, rate-limiting enumeration requests to the authLoginAction!login.do endpoint and hindering systematic discovery of valid accounts.
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 correct authorization logic after parsing/canonicalization to prevent bypasses.
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.
Security testing can detect this weakness but does not prevent it by itself.
Secure development lifecycle requires proper URL parsing and canonicalization before authorization checks.
Application security requirements should mandate correct authorization order after canonicalization.
Secure architecture principles include proper request handling order to prevent bypasses.
Secure coding practices directly address canonicalization and authorization sequencing flaws.
Information access restriction is undermined when authorization occurs before canonicalization.