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LiteLLM Flaws Let Attackers Execute Code as Root and Steal Cloud Credentials
Wiz disclosed LiteLLM flaws including CVE-2026-59822 MCP auth bypass, now in CISA KEV, enabling root RCE and AWS credential theft from exposed gateways.
Wiz researchers disclosed weaknesses in the open-source LiteLLM AI gateway: CVE-2026-59821 allows authenticated administrators to execute code as root via the Custom Code Guardrails feature, while CVE-2026-59822 is an MCP authentication bypass where a meaningless Bearer token establishes a valid session. The MCP bypass was observed in the wild through Wiz honeypots and added to CISA's Known Exploited Vulnerabilities catalog on September 2. A scan of 3,074 internet-facing instances found 294 (9.6%) accepting the default master key sk-1234 or lacking authentication. Fixes shipped in LiteLLM 1.82.0 (guardrail hardening) and 1.84.0 (MCP bypass); exposed instances can leak provider API keys and temporary AWS IAM credentials via the pass-through feature and metadata service, with a related config-check issue tracked as CVE-2026-35029.
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The FTC wants to regulate AI for ideological bias
FTC proposes classifying ideological bias in AI systems as an unfair or deceptive practice, drawing criticism over legal authority and censorship risks.
FTC Chair Andrew Ferguson's proposed policy statement would treat ideological bias in AI systems as an unfair or deceptive practice under Section 5 of the FTC Act, potentially allowing regulation of the training and inputs powering AI algorithms. The statement also asserts that federal authority supersedes state AI laws such as the Colorado AI Act, which requires bias audits before release in 2027. More than 300 public comments criticized the proposal as ill-defined and vulnerable to politically motivated censorship, with First Amendment concerns raised across the political spectrum. Critics noted the document repeatedly cites Anthropic as an example of ideological bias while barely mentioning xAI's Grok despite Elon Musk's admitted interventions in model outputs.
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CareMirror: Bringing Caregiver Wellbeing into the Dementia Care Ecosystem
Interview study with 14 dementia caregivers probes CareMirror wellbeing ecosystem, revealing demands for control over clinical sharing and AI boundaries.
CareMirror is an envisioned caregiver wellbeing ecosystem with interconnected caregiver- and clinician-facing interfaces for longitudinal reflection, personalized support, and caregiver-controlled sharing. Semi-structured interviews with 14 family caregivers used the system as a design probe. Caregivers valued wellbeing attention and clinical visibility but found repeated reflection burdensome and worried automatic clinical sharing would inhibit candid disclosure, expecting AI to support rather than replace caregiver and clinician judgment.
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Invisible AI Prompts Trigger Court Sanctions
A Connecticut litigant hid white-font prompt injections in court filings to sway AI systems; the judge sanctioned him by revoking e-filing privileges.
A self-represented plaintiff hid prompt injection instructions in 3-point white text within court filings, telling any AI model reading the documents to agree with his filings and grant him relief. The judge called it serious litigation abuse and sanctioned him by revoking electronic filing privileges. It is reportedly the first documented prompt injection attack against a US court and the first sanction for attempting one.
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