Do you know what your employees have shared with your company's LLM? The code, the credentials, the
open-source
| Source: Mastodon | Original article
A demo at BSides312 in Chicago showed that corporate large‑language models (LLMs) can be turned into inadvertent data vaults. Security researcher Sharon Shama unveiled an open‑source utility that scrapes a company’s internal LLM chat logs and extracts everything employees have typed – source code snippets, API keys, proprietary documents and other sensitive artefacts. The tool, built on the public APIs of popular LLM platforms, parses conversation histories, reconstructs file attachments and presents the material in a searchable archive. In a live run, Shama fed the scraper a modest test deployment of an internal chatbot and recovered dozens of credential strings and code fragments that had been shared in routine troubleshooting sessions.
The demonstration matters because enterprises are rapidly rolling out custom LLMs for help‑desk support, software development assistance and knowledge‑base queries, often without robust governance. While the models boost productivity, they also retain user inputs by default, creating a hidden repository that is far more accessible than traditional file servers. If an insider or a compromised account can query the model, the entire corpus of confidential information becomes exposed with a single prompt. The open‑source nature of Shama’s tool means that the same capability can be weaponised by malicious actors who gain limited access to a corporate LLM.
Watch for a wave of policy revisions and technical safeguards in the coming months. Vendors are already promising “conversation expiration” and “data‑masking” features, but adoption will hinge on clear audit logs and role‑based access controls. Security teams should inventory every LLM endpoint, enforce strict data‑handling guidelines, and consider deploying external monitoring solutions that flag the ingestion of privileged material. The BSides312 demo underscores that controlling what employees feed into AI assistants is now as critical as protecting the endpoints they use.
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