Training data can serve as a governance lever for frontier AI through five proposed mechanisms: canary tokens, mandatory filtering, dataset reporting, data security, and know-your-customer rules.
A Lightweight Adaptable DNS Channel for Covert Data Transmission
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abstract
Due to the vital role of security in online communications and this fact that attackers are developing their tools, modernizing the security tools is an essential. The efficiency of crypto systems has been proven after years, however one may need to communicate stealthy without drawing attention especially in transferring secret data such as keys. Covert channels are suitable tools that used to conceal the existence of data besides end communication parties by employing principles of steganography. They can make secure communications with obscurity. Working stealthy and providing an acceptable throughput are issues in designing covert channels. The DNS protocol properties like its necessity for running applications and the availability can provide aforementioned issues decently. In this paper, we proposed a storage covert channel which uses DNS protocol as a media for transferring data. The key features include connection establishment, adaptability with network environment, implying a lightweight obfuscation method and HMAC to meet confidentiality and integrity. Experimental results show the proposed channel statistics are well adapted with normal traffics. The channel has an average capacity of 2.65 bytes of data per packet.
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Towards Data Governance of Frontier AI Models
Training data can serve as a governance lever for frontier AI through five proposed mechanisms: canary tokens, mandatory filtering, dataset reporting, data security, and know-your-customer rules.