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Alleged LLMjacking Targets AI Cloud Services with Stolen Credentials

2 news reports Snapshot 7 Sep 2026

In brief

An AI system built by Oai Reverse Proxy Tool Creators and Llmjacking Reverse Proxy Tool Creators and deployed by Llmjacking Attackers Exploiting Laravel and Entities Engaging In Russian Sanctions Evasion allegedly harmed Laravel Users, Laravel Cve 2021 3129 Users and 2 others.

Risk domain
Malicious Actors & Misuse Fraud, scams, and targeted manipulation
Occurred
Coverage
2 reportsMay 2024 - Sep 2024

What happened

Attackers reportedly exploited stolen cloud credentials obtained through a vulnerable Laravel system (CVE-2021-3129) to allegedly abuse AI cloud services, including Anthropic’s Claude and AWS Bedrock, in a scheme referred to as “LLMjacking.” The attackers are said to have monetized access through reverse proxies, reportedly inflating victim costs to as much as $100,000 per day. Additionally, they allegedly bypassed sanctions, enabled LLM models, and evolved techniques to evade detection and logg

Laws that address this harm

Policy angle: Classified under Malicious Actors & Misuse (Fraud, scams, and targeted manipulation) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (2)

Titles link to the original publisher; report text is not reproduced here.

  1. LLMjacking: Stolen Cloud Credentials Used in New AI Attack
    sysdig.com · Alessandro Brucato, Sysdig
  2. The Growing Dangers of LLMjacking: Evolving Tactics and Evading Sanctions
    sysdig.com · Sysdig Threat Research Team

Who was involved

Alleged harmed party
Laravel Users, Laravel Cve 2021 3129 Users, Cloud Llm Users, Cloud Llm Service Providers

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
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Sectors
—
Countries
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Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 4.3.

  • Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    "IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    "Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Impersonation/identity theft

    "Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    "Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Misinformation and Manipulation

    "Recent studies have demonstrated that LLMs can be exploited to craft deceptive narratives with levels of persuasiveness similar to human-generated content (Pan et al., 2023b; Spitale et al., 2023), to fabri- cate fake n...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Cybersecurity

    "LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #898 on the AI Incident Database · all 2 reports