T1608.007: AI Result Poisoning
Adversaries may manipulate publicly accessible web content to influence generative AI systems or AI-powered search engines into retrieving, citing, or recommending attacker-controlled information.[1][2]
Unlike traditional SEO Poisoning, which primarily attempts to influence the ordering of conventional search results, AI Result Poisoning targets systems that synthesize information from multiple sources and provide a direct answer to the user. Adversaries may create malicious or misleading websites, publish fabricated claims, generate supporting articles, or establish multiple apparently independent sources that reinforce the same information. These sources may then be retrieved by AI systems using web search or retrieval-augmented generation and incorporated into generated responses.
Adversaries may use this technique to recommend or link to phishing sites, fraudulent online stores, malicious downloads, impersonation pages, or other attacker-controlled infrastructure.[3][4][5] It can also be used to promote misinformation or fabricated claims without requiring access to the underlying AI model, system prompt, or application infrastructure.[6] Because the malicious content can resemble ordinary marketing, editorial, or informational material, traditional prompt injection and malware-focused detections may not identify the activity.
Security context for executives and security teams
T1608.007: AI Result Poisoning describes Adversaries may manipulate publicly accessible web content to influence generative AI systems or AI-powered search engines into retrieving, citing, or recommending attacker-controlled information.(Citation: Digiday AI Result Poisoning)(Citation: Netcraft AI Summaries) Unlike traditional [SEO Poisoning](https://attack.mitre.org/techniques/T1608/006), which primarily attempts to influence the ordering of conventional search results, AI Result Poisoning targets systems that synthesize information from multiple sources...
Executive priority
T1608.007: AI Result Poisoning is an official MITRE ATT&CK technique. Glexia treats it as defensive behavior context for prioritizing monitoring, control validation, and response planning without using the object by itself as an attribution claim.
Technical view
Security teams should validate T1608.007: AI Result Poisoning by reviewing the official ATT&CK relationships, mapped tactics (resource-development), supported platforms (PRE), and available local telemetry before making detection or mitigation decisions.
Likely telemetry
- Official ATT&CK relationships and object metadata
Detection direction
- Validate whether T1608.007: AI Result Poisoning appears in your detection coverage and tabletop scenarios.
- Use the object to align executive risk language with SOC, incident response, and detection engineering work.
- Do not treat ATT&CK relationship context as attribution without corroborating evidence.
Mitigation priorities
- Map the object to existing controls and identify missing telemetry or response ownership.
- Prioritize mitigations that reduce exposure on the listed platforms and tactics.
- Review adjacent ATT&CK relationships before changing policy, detections, or reporting language.
Additional notes and limits
Baseline Glexia take generated from the official MITRE ATT&CK STIX object, source hash, tactics, platforms, and detection fields. It is safe to replace with a richer model-generated take for the same source hash later.
This baseline take is source-grounded and schema-validated, but it does not include environment-specific telemetry, incident evidence, or threat-intelligence corroboration.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
AI Result Poisoning
Adversaries may manipulate publicly accessible web content to influence generative AI systems or AI-powered search engines into retrieving, citing, or recommending attacker-controlled information.[1][2]
Unlike traditional SEO Poisoning, which primarily attempts to influence the ordering of conventional search results, AI Result Poisoning targets systems that synthesize information from multiple sources and provide a direct answer to the user. Adversaries may create malicious or misleading websites, publish fabricated claims, generate supporting articles, or establish multiple apparently independent sources that reinforce the same information. These sources may then be retrieved by AI systems using web search or retrieval-augmented generation and incorporated into generated responses.
Adversaries may use this technique to recommend or link to phishing sites, fraudulent online stores, malicious downloads, impersonation pages, or other attacker-controlled infrastructure.[3][4][5] It can also be used to promote misinformation or fabricated claims without requiring access to the underlying AI model, system prompt, or application infrastructure.[6] Because the malicious content can resemble ordinary marketing, editorial, or informational material, traditional prompt injection and malware-focused detections may not identify the activity.
How security teams should use this page
Treat this object as behavior context, not an attribution claim. Validate the related groups, software, data sources, and mitigations against official ATT&CK relationships and your own telemetry before making control-coverage decisions.
Related techniques
This mirrors the MITRE pattern of making group, software, campaign, and technique relationships scannable. Relationship notes come from mirrored ATT&CK relationship text when available.
| Domain | ID | Name | Relationship / procedure |
|---|---|---|---|
| Enterprise | T1608 | Stage Capabilities | This object subtechnique of Stage Capabilities. |
All related ATT&CK context
Mitigation direction
Object version and sync metadata
The fields below describe the current mirrored snapshot. When Glexia retains multiple ATT&CK source imports, you can open the table to compare the same object across releases (hashes and MITRE timestamps). For MITRE’s own release notes and roadmap, see ATT&CK resources — Updates.
Imported snapshots across ATT&CK releases(1)
| Release | Bundle imported | Object version | Modified | Status | Raw hash |
|---|---|---|---|---|---|
| 20.0 | 1.0 | Current bundle | 60962ed0a43e… |
Mirrored ATT&CK source object
The raw object is retained through the mirrored ATT&CK source bundle and object hash. The raw endpoint returns the exact object from the mirrored bundle when available.
External references and citations
MITRE external references are preserved separately from Glexia analysis so citations remain traceable to their original source records.
- [1]Digiday AI Result Poisoning
Sam Bradley. (2026, July 6). WTF is AI poisoning?. Retrieved September 18, 2026.
Open source URL - [2]Netcraft AI Summaries
Ginny Spicer. (2026, August 3). Threat Actors are Finding Their Way into Your AI Summaries. Retrieved September 18, 2026.
Open source URL - [3]dev.to Abusing AI Chatbot Recommendations
Dennis Kim. (2026, May 27). Cryptojacking Abusing AI Chatbot Recommendations — A New Delivery Vector Beyond Search Poisoning. Retrieved September 18, 2026.
Open source URL - [4]darkreading geo poisoning
Alexander Culafi. (2025, December 10). ClickFix Style Attack Uses Grok, ChatGPT for Malware Delivery. Retrieved September 18, 2026.
Open source URL - [5]darkreading ai result poisoning
Alexander Culafi. (2025, December 10). ClickFix Style Attack Uses Grok, ChatGPT for Malware Delivery. Retrieved September 18, 2026.
Open source URL - [6]Lasso Security AI Engine Poisoning
Eliya Saban. (2026, June 24). Exploiting GEO to Push Harmful Claims into AI-Generated Answers. Retrieved September 18, 2026.
Open source URL - [7]mitre-attackT1608.007Open source URL
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