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LLM proxy

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  1. 1
    Why developers are running their own LLM proxies▼If you don't run your own LLM proxy, how do you know whats going on?YhnCultureTelevision30228 min ago

    A developer argues that anyone building on large language models should route their API traffic through a self-hosted proxy, otherwise they have no visibility into what is actually being sent and returned. The piece argues logging requests and responses is essential for debugging, cost control and privacy. The debate has drawn attention among AI developers who rely on third-party providers like OpenAI and Anthropic without inspecting the traffic flowing to them.

  2. 2
    Compromised LiteLLM 1.107.0 on PyPI Found to Steal Credentials●Compromised LiteLLM 1.107.0 on PyPI Installs a Credential Stealer and Backdoor Article URL: https:// safedep.io/litellm-MmastodonTechnologyCybersecurity221 h ago

    Security researchers at SafeDep report that version 1.107.0 of LiteLLM published to the Python Package Index was compromised and installed a credential stealer and backdoor on systems using it. Developers who pulled the package during the affected window are being urged to check their environments, rotate exposed API keys and secrets, and pin to a clean version.

  3. 3
    The case for running your own LLM proxy●If you don't run your own LLM proxy, how do you know whats going on? Article URL: https:// jrdsgl.com/if-you-dont-run-yoMmastodonBusinessStartups03 d ago

    A new article argues that developers who rely on third-party endpoints or managed gateways have little real visibility into how their large language model calls are being handled, and that running your own LLM proxy is the only way to actually see what is going on. It is sparking discussion among developers about observability, logging, and control in AI infrastructure.

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