MJB A.I CyberSecurity Scoring
03/09/2026
Access Monitoring Plan
Access Monitoring Plan
Minnesota Judicial Branch has 20.0% fewer incidents than the average of same-industry companies with at least one recorded incident.
Minnesota Judicial Branch has 0.99% fewer incidents than the average of all companies with at least one recorded incident.
Minnesota Judicial Branch reported 1 incidents this year: 0 cyber attacks, 0 ransomware, 0 vulnerabilities, 1 data breaches, compared to industry peers with at least 1 incident.
Administration of Justice
The U.S. Courts, located in courthouses across the nation, safeguard the constitutional rights and liberties of the public. Judges, court clerks, federal public defenders, law clerks, probation and pretrial services officers, technology specialists, human resources and budget specialists, administrative staff, and many others with a variety of skills and talents work in support of this mission. We invite you to learn more about us and join the people who work to help ensure equal justice under the law. About the Courts As Guardians of the Constitution, the U.S. Courts address cases and controversies that can impact the lives of all Americans. The U.S. Courts: - Help individuals and businesses who cannot pay their debts. - Resolve civil disputes involving failure to meet legal agreements. - Decide criminal cases alleging violations of federal criminal laws. - Conduct naturalization ceremonies for new U.S. citizens. - Work with criminal defendants and offenders in the probation and pretrial system. - Provide federal public defenders to those who cannot afford legal counsel. - Call those 18 years and older to serve on juries. - Call witnesses to testify in civil or criminal cases. The U.S. Courts System is comprised of the U.S. Supreme Court; 13 U.S. Courts of Appeals; 94 U.S. District Courts, which include U.S. Bankruptcy Courts; Courts of Special Jurisdiction; U.S. Probation and U.S. Pretrial Services Offices; Federal Public Defenders Offices; and several support agencies, including the Administrative Office of the U.S. Courts, the Federal Judicial Center, and the U.S. Sentencing Commission.
Latest updates, reports, and threat intel affecting the global network.
A US government attorney expressed unusual frustration in a courtroom proceeding about the difficulty in ensuring Immigration and Customs...
The legal profession is undergoing a significant transformation with the integration of advanced technologies into the practice of law.
AI's growing abilities to create realistic videos, images, documents and audio have judges worried about the trustworthiness of evidence in...
The federal judiciary is strengthening online docket security after cyberattacks exposed sensitive case data, raising national security...
U.S. Senator Ron Wyden on Monday asked Chief U.S. Supreme Court Justice John Roberts to commission an independent review of the federal...
A U.S. federal court filing system has experienced a security breach after suspected nation-state actors potentially accessed highly...
Federal officials are scrambling to assess the damage and address flaws in a sprawling, heavily used computer system long known to have...
The identities of confidential court informants are feared compromised in a series of breaches across multiple U.S. states.
GAMING Canterbury Park Racetrack and Casino, Shakopee, announced that Jennifer Lauerman has been named vice president of marketing and...
Dify is an open-source LLM app development platform. Prior to 1.16.0, the PUT /console/api/apps/<app_id>/server endpoint in api/controllers/console/app/mcp_server.py used AppMCPServerController.put() to retrieve an AppMCPServer by the client-supplied server ID without verifying that the server belonged to the requested application and tenant. An authenticated workspace member could therefore change another application's MCP server status and parameters, potentially redirecting data or disabling the service. This issue is fixed in version 1.16.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, a caller can use the request-level media_io_kwargs field to select the GLMGA video backend and supply large values for the fps and max_frames options without a strict work ceiling. GLMGA constructs and deduplicates an attacker-sized pre-decode frame-index list, allowing a compact request and tiny valid video to consume disproportionate CPU time and memory in the shared media-loading executor. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the Rust frontend's track_http_metrics middleware records the raw HTTP method token as a Prometheus label for requests reaching registered routes. An unauthenticated attacker can send unique arbitrary method tokens to unguarded routes such as /tokenize, causing Prometheus's Family::get_or_create function to permanently create counter and histogram label sets. Those label sets increase process memory usage and enlarge the /metrics response until the service or monitoring path is exhausted. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. From 0.24.0 until 0.30.0, the Qwen2VLVideoBackend and Qwen3VLVideoBackend classes accept request-level values for the media_io_kwargs.video.max_frames and media_io_kwargs.video.fps fields without enforcing server-side ceilings. An unauthenticated caller can submit these values to the /tokenize endpoint, causing the sampler to decode every frame selected from attacker-controlled video input, consume disproportionate frontend memory, and potentially terminate the API process before scheduling or admission control. The Rust frontend is not affected because it rejects the media_io_kwargs field. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by the ngram_gpu speculative-decoding mode can pass a negative token to guidance validation, and the Rust frontend can admit empty structured-output values that the Python frontend rejects, allowing ordinary constrained-generation requests to terminate the shared engine. This issue is fixed in version 0.30.0.
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