Security Resilience

CVE-2024-3465

SQLi in Oretnom23 Laundry Shop Management System 1.0

Public PoCSQLi
Published
08 April 2024
Modified
17 June 2026
CVSS Score v3.1 6.3
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L
EPSS Score 0.0076 54th percentile
Risk Priority 50 floored blend · peak EPSS

CVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.

Summary

CVE-2024-3465 is a medium-severity SQL Injection (CWE-89) vulnerability in Oretnom23 Laundry Shop Management System. Its CVSS base score is 6.3 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Steganography (T1001.002); ranked in the top 46% of CVEs by exploit likelihood; 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 SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

A vulnerability was found in SourceCodester Laundry Management System 1.0. It has been classified as critical. Affected is the function laporan_filter of the file /application/controller/Transaki.php. The manipulation of the argument dari/sampai leads to sql injection. It is possible to launch…

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the attack remotely. The exploit has been disclosed to the public and may be used. VDB-259746 is the identifier assigned to this vulnerability.

CWE(s)

Related Threats

MITRE ATT&CK Enterprise Techniques

T1001.002 Steganography Command And Control
Adversaries may use steganographic techniques to hide command and control traffic to make detection efforts more difficult.
T1001.003 Protocol or Service Impersonation Command And Control
Adversaries may impersonate legitimate protocols or web service traffic to disguise command and control activity and thwart analysis efforts.
T1003 OS Credential Dumping Credential Access
Adversaries may attempt to dump credentials to obtain account login and credential material, normally in the form of a hash or a clear text password.
T1003.001 LSASS Memory Credential Access
Adversaries may attempt to access credential material stored in the process memory of the Local Security Authority Subsystem Service (LSASS).
T1003.002 Security Account Manager Credential Access
Adversaries may attempt to extract credential material from the Security Account Manager (SAM) database either through in-memory techniques or through the Windows Registry where the SAM database is stored.
T1003.003 NTDS Credential Access
Adversaries may attempt to access or create a copy of the Active Directory domain database in order to steal credential information, as well as obtain other information about domain members such as devices, users, and access rights.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2024-3445Same product: Oretnom23 Laundry Shop Management System
CVE-2024-3464Same product: Oretnom23 Laundry Shop Management System
CVE-2024-3466Same product: Oretnom23 Laundry Shop Management System
CVE-2023-2409Same vendor: Oretnom23
CVE-2024-9315Same vendor: Oretnom23
CVE-2023-2668Same vendor: Oretnom23
CVE-2024-5378Same vendor: Oretnom23
CVE-2024-7667Same vendor: Oretnom23
CVE-2023-24728Same vendor: Oretnom23
CVE-2024-5894Same vendor: Oretnom23

Affected Assets

oretnom23
laundry shop management system
1.0

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)

—

Validate
Prove the fix (OWASP ASVS)
  • V6.2.5

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.

Input validation directly stops untrusted data from reaching SQL query construction without neutralization.

Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.

System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.

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.

PR.PS-06 mostly match
blocks

Secure SDLC practices directly target injection flaws during coding and review so largely prevent CWE-89 introduction, yet the single broad outcome leaves residual risk from incomplete neutralization techniques or missed edge cases.

PR.AT-02 partial match
blocks

Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.

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.

removes
removes
removes
removes
blocks

A.5.6 only improves knowledge flow, awareness of threats/patches and access to specialist advice; it does not stop SQL injection from being exploitable in code that still contains the flaw.

References