CWE-203
Observable Discrepancy
The product behaves differently or sends different responses under different circumstances in a way that is observable to an unauthorized actor.
CVE-2024-47057 (GCVE-0-2024-47057)
Vulnerability from cvelistv5 – Published: 2025-05-28 17:23 – Updated: 2025-05-29 18:59- CWE-203 - Observable Discrepancy
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CVE-2024-47869 (GCVE-0-2024-47869)
Vulnerability from cvelistv5 – Published: 2024-10-10 22:16 – Updated: 2024-10-11 15:08- CWE-203 - Observable Discrepancy
| URL | Tags |
|---|---|
| https://github.com/gradio-app/gradio/security/adv… | x_refsource_CONFIRM |
| Vendor | Product | Version | |
|---|---|---|---|
| gradio-app | gradio |
Affected:
< 4.44
|
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CVE-2024-49358 (GCVE-0-2024-49358)
Vulnerability from cvelistv5 – Published: 2024-10-24 21:25 – Updated: 2024-10-25 17:28- CWE-203 - Observable Discrepancy
| URL | Tags |
|---|---|
| https://github.com/IceWhaleTech/ZimaOS/security/a… | x_refsource_CONFIRM |
| https://youtu.be/BClx2u8DMfM | x_refsource_MISC |
| Vendor | Product | Version | |
|---|---|---|---|
| IceWhaleTech | ZimaOS |
Affected:
<= 1.2.4
|
|
| icewhaletech | zimaos |
Affected:
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CVE-2024-5124 (GCVE-0-2024-5124)
Vulnerability from cvelistv5 – Published: 2024-06-06 18:54 – Updated: 2025-05-20 14:04- CWE-203 - Observable Discrepancy
| Vendor | Product | Version | |
|---|---|---|---|
| gaizhenbiao | gaizhenbiao/chuanhuchatgpt |
Affected:
unspecified , < 20240628
(custom)
|
|
| gaizhenbiao | chuanhuchatgpt |
Affected:
all
cpe:2.3:a:gaizhenbiao:chuanhuchatgpt:*:*:*:*:*:*:*:* |
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CVE-2024-51477 (GCVE-0-2024-51477)
Vulnerability from cvelistv5 – Published: 2025-03-28 23:51 – Updated: 2025-09-01 01:00- CWE-203 - Observable Discrepancy
| URL | Tags |
|---|---|
| https://www.ibm.com/support/pages/node/7185058 | vendor-advisorypatch |
| Vendor | Product | Version | |
|---|---|---|---|
| IBM | InfoSphere Information Server |
Affected:
11.7
cpe:2.3:a:ibm:infosphere_information_server:11.7:*:*:*:*:*:*:* |
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CVE-2024-54002 (GCVE-0-2024-54002)
Vulnerability from cvelistv5 – Published: 2024-12-04 15:33 – Updated: 2024-12-04 21:39- CWE-203 - Observable Discrepancy
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| https://github.com/DependencyTrack/dependency-tra… | x_refsource_CONFIRM |
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|
|
| dependencytrack | dependency-track |
Affected:
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CVE-2024-9513 (GCVE-0-2024-9513)
Vulnerability from cvelistv5 – Published: 2024-10-04 12:31 – Updated: 2024-10-07 20:27- CWE-203 - Information Exposure Through Discrepancy
| URL | Tags |
|---|---|
| https://vuldb.com/?id.279212 | vdb-entrytechnical-description |
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| https://vuldb.com/?submit.413498 | third-party-advisory |
| Vendor | Product | Version | |
|---|---|---|---|
| Netadmin Software | NetAdmin IAM |
Affected:
3.0
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CVE-2025-0361 (GCVE-0-2025-0361)
Vulnerability from cvelistv5 – Published: 2025-04-08 05:38 – Updated: 2025-04-08 14:50- CWE-203 - Observable Discrepancy
| Vendor | Product | Version | |
|---|---|---|---|
| Axis Communications AB | AXIS OS |
Affected:
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CVE-2025-11145 (GCVE-0-2025-11145)
Vulnerability from cvelistv5 – Published: 2025-10-24 14:25 – Updated: 2025-10-28 14:05| Vendor | Product | Version | |
|---|---|---|---|
| CBK Soft Software Hardware Electronic Computer Systems Industry and Trade Inc. | enVision |
Affected:
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CVE-2025-11443 (GCVE-0-2025-11443)
Vulnerability from cvelistv5 – Published: 2025-10-08 07:32 – Updated: 2025-10-08 13:16| URL | Tags |
|---|---|
| https://vuldb.com/?id.327380 | vdb-entry |
| https://vuldb.com/?ctiid.327380 | signaturepermissions-required |
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| https://docs.google.com/document/d/1GUjJA9vUbsXUn… | exploit |
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"value": "Eine Schwachstelle wurde in JhumanJ OpnForm up to 1.9.3 gefunden. Hierbei geht es um eine nicht exakt ausgemachte Funktion der Datei /api/password/email der Komponente Forgotten Password Handler. Durch Manipulation mit unbekannten Daten kann eine information exposure through discrepancy-Schwachstelle ausgenutzt werden. Die Umsetzung des Angriffs kann dabei \u00fcber das Netzwerk erfolgen. Ein Angriff erfordert eine vergleichsweise hohe Komplexit\u00e4t. Sie gilt als schwierig ausnutzbar. Der Exploit steht zur \u00f6ffentlichen Verf\u00fcgung."
}
],
"metrics": [
{
"cvssV4_0": {
"baseScore": 6.3,
"baseSeverity": "MEDIUM",
"vectorString": "CVSS:4.0/AV:N/AC:H/AT:N/PR:N/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N/E:P",
"version": "4.0"
}
},
{
"cvssV3_1": {
"baseScore": 3.7,
"baseSeverity": "LOW",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:N/A:N/E:P/RL:X/RC:C",
"version": "3.1"
}
},
{
"cvssV3_0": {
"baseScore": 3.7,
"baseSeverity": "LOW",
"vectorString": "CVSS:3.0/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:N/A:N/E:P/RL:X/RC:C",
"version": "3.0"
}
},
{
"cvssV2_0": {
"baseScore": 2.6,
"vectorString": "AV:N/AC:H/Au:N/C:P/I:N/A:N/E:POC/RL:ND/RC:C",
"version": "2.0"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-203",
"description": "Information Exposure Through Discrepancy",
"lang": "en",
"type": "CWE"
}
]
},
{
"descriptions": [
{
"cweId": "CWE-200",
"description": "Information Disclosure",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2025-10-08T07:32:07.900Z",
"orgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"shortName": "VulDB"
},
"references": [
{
"name": "VDB-327380 | JhumanJ OpnForm Forgotten Password email information exposure",
"tags": [
"vdb-entry"
],
"url": "https://vuldb.com/?id.327380"
},
{
"name": "VDB-327380 | CTI Indicators (IOB, IOC, TTP, IOA)",
"tags": [
"signature",
"permissions-required"
],
"url": "https://vuldb.com/?ctiid.327380"
},
{
"name": "Submit #666890 | GitHub OpnForm 1.9.3 Account Enumeration on Password Recovery",
"tags": [
"third-party-advisory"
],
"url": "https://vuldb.com/?submit.666890"
},
{
"tags": [
"exploit"
],
"url": "https://docs.google.com/document/d/1GUjJA9vUbsXUngAv6ySsbCIhVynf8_djardLZYEDOe0/edit?tab=t.0#heading=h.my0ldciyllp"
}
],
"timeline": [
{
"lang": "en",
"time": "2025-10-07T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2025-10-07T02:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2025-10-07T15:22:53.000Z",
"value": "VulDB entry last update"
}
],
"title": "JhumanJ OpnForm Forgotten Password email information exposure"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2025-11443",
"datePublished": "2025-10-08T07:32:07.900Z",
"dateReserved": "2025-10-07T13:17:33.700Z",
"dateUpdated": "2025-10-08T13:16:46.766Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
Mitigation ID: MIT-46
Phase: Architecture and Design
Strategy: Separation of Privilege
Description:
- Compartmentalize the system to have "safe" areas where trust boundaries can be unambiguously drawn. Do not allow sensitive data to go outside of the trust boundary and always be careful when interfacing with a compartment outside of the safe area.
- Ensure that appropriate compartmentalization is built into the system design, and the compartmentalization allows for and reinforces privilege separation functionality. Architects and designers should rely on the principle of least privilege to decide the appropriate time to use privileges and the time to drop privileges.
Mitigation ID: MIT-39
Phase: Implementation
Description:
- Ensure that error messages only contain minimal details that are useful to the intended audience and no one else. The messages need to strike the balance between being too cryptic (which can confuse users) or being too detailed (which may reveal more than intended). The messages should not reveal the methods that were used to determine the error. Attackers can use detailed information to refine or optimize their original attack, thereby increasing their chances of success.
- If errors must be captured in some detail, record them in log messages, but consider what could occur if the log messages can be viewed by attackers. Highly sensitive information such as passwords should never be saved to log files.
- Avoid inconsistent messaging that might accidentally tip off an attacker about internal state, such as whether a user account exists or not.
CAPEC-189: Black Box Reverse Engineering
An adversary discovers the structure, function, and composition of a type of computer software through black box analysis techniques. 'Black Box' methods involve interacting with the software indirectly, in the absence of direct access to the executable object. Such analysis typically involves interacting with the software at the boundaries of where the software interfaces with a larger execution environment, such as input-output vectors, libraries, or APIs. Black Box Reverse Engineering also refers to gathering physical side effects of a hardware device, such as electromagnetic radiation or sounds.