FKIE_CVE-2026-96561
Vulnerability from fkie_nvd - Published: 2026-10-01 04:18 - Updated: 2026-10-01 15:17
Severity
Summary
The AI Engine – The Chatbot, AI Framework & MCP for WordPress plugin for WordPress is vulnerable to Stored Cross-Site Scripting in versions up to, and including, 3.8.0 This is due to a chain of missing input neutralization and output escaping across the /mwai-ui/v1/chats/submit REST endpoint, the PHP error-log parser (MeowKit_MWAI_Helpers::php_error_logs), the Advisor task (Meow_MWAI_Modules_Advisor::run_advisor), and the Advisor dashboard widget (advisor_metabox): the server-parameter denylist in chat_submit strips only exact key names such as 'model' while convert_keys() later canonicalizes 'model_' back to 'model', allowing an unauthenticated caller to place an attacker-controlled string (including CR/LF) into $query->model; final_checks() throws an Exception whose message embeds that raw string, and the non-streaming, non-admin catch branch writes it to the PHP error log unmodified — creating a forged log line that the plugin's own parser subsequently returns as recent PHP-error content; run_advisor() then appends that content verbatim to the AI prompt (indirect prompt injection — CWE-1427), the returned JSON is stored in the mwai_advisor_data option with no schema validation or HTML sanitization, and advisor_metabox() concatenates the resulting 'title' and 'description' values directly into the WordPress dashboard widget without esc_html(), wp_kses(), or equivalent escaping. This makes it possible for unauthenticated attackers to inject arbitrary web scripts in pages that will execute whenever an administrator accesses the WordPress dashboard.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"product": "AI Engine \u2013 The Chatbot, AI Framework \u0026 MCP for WordPress",
"vendor": "tigroumeow",
"versions": [
{
"lessThanOrEqual": "3.8.0",
"status": "affected",
"version": "0",
"versionType": "semver"
}
]
}
],
"source": "security@wordfence.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The AI Engine \u2013 The Chatbot, AI Framework \u0026 MCP for WordPress plugin for WordPress is vulnerable to Stored Cross-Site Scripting in versions up to, and including, 3.8.0 This is due to a chain of missing input neutralization and output escaping across the /mwai-ui/v1/chats/submit REST endpoint, the PHP error-log parser (MeowKit_MWAI_Helpers::php_error_logs), the Advisor task (Meow_MWAI_Modules_Advisor::run_advisor), and the Advisor dashboard widget (advisor_metabox): the server-parameter denylist in chat_submit strips only exact key names such as \u0027model\u0027 while convert_keys() later canonicalizes \u0027model_\u0027 back to \u0027model\u0027, allowing an unauthenticated caller to place an attacker-controlled string (including CR/LF) into $query-\u003emodel; final_checks() throws an Exception whose message embeds that raw string, and the non-streaming, non-admin catch branch writes it to the PHP error log unmodified \u2014 creating a forged log line that the plugin\u0027s own parser subsequently returns as recent PHP-error content; run_advisor() then appends that content verbatim to the AI prompt (indirect prompt injection \u2014 CWE-1427), the returned JSON is stored in the mwai_advisor_data option with no schema validation or HTML sanitization, and advisor_metabox() concatenates the resulting \u0027title\u0027 and \u0027description\u0027 values directly into the WordPress dashboard widget without esc_html(), wp_kses(), or equivalent escaping. This makes it possible for unauthenticated attackers to inject arbitrary web scripts in pages that will execute whenever an administrator accesses the WordPress dashboard."
}
],
"id": "CVE-2026-96561",
"lastModified": "2026-10-01T15:17:37.143",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.2,
"baseSeverity": "HIGH",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 2.7,
"source": "security@wordfence.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-96561",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-10-01T14:17:52.566760Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-10-01T04:18:22.110",
"references": [
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/engines/core.php#L533"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/modules/advisor.php#L156"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/modules/advisor.php#L162"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/modules/advisor.php#L216"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/modules/chatbot.php#L122"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/modules/chatbot.php#L299"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/modules/chatbot.php#L597"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/query/base.php#L360"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/classes/rest.php#L464"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/ai-engine/tags/3.8.0/common/helpers.php#L251"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/changeset?old_path=%2Fai-engine%2Ftags%2F3.8.0\u0026new_path=%2Fai-engine%2Ftags%2F3.8.1"
},
{
"source": "security@wordfence.com",
"url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/910e51da-6ee2-45d5-837b-7fea80f79390?source=cve"
}
],
"sourceIdentifier": "security@wordfence.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-79"
}
],
"source": "security@wordfence.com",
"type": "Secondary"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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