102 citations · 543 across the 71 of their papers we have counts for
84 papers
Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker
Douglas Leith
Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive. We evaluate this for the protobuf messages that an i…
Poor Privacy Practices Of The Apple App Store: Cookies, Advertising and Tracking Of Users
Douglas Leith
We analyse the data that the Apple App Store sends to and receives from Apple servers. We find that multiple cookies are sent by Apple servers and stored on the handset. Adverts wi…
Between the Commits: Process, Error, and Claim Reliability in a Wholly AI-Authored Codebase
Douglas Leith
We present: (i) a new dataset consisting of the full development history of a 21,000-line Python tool built entirely by Claude AI, with no human-authored code or tests, (ii) two co…
SMS-delivered network-initiated SUPL on Pixel 8: a privacy assessment
Douglas Leith
A SUPL\_INIT message is a network-initiated trigger that can be sent to a handset using an SMS to unilaterally start a location session: on receipt, the handset is instructed to de…
The Quality of Claude AI-authored Python Tests Is Not Weaker Than Human-authored Tests
Douglas J. Leith
We evaluate the quality of Claude AI-written Python tests against human-written Python tests from two established open-source projects Django and Pandas. Hundreds of tests per corp…
Persona-Conditioned Adversarial Prompting (PCAP): Multi-Identity Red-Teaming for Enhanced Adversarial Prompt Discovery
Cristian Morasso, Anisa Halimi, Muhammad Zaid Hameed +1
Existing automated red-teaming pipelines often miss attacks that depend on attacker identity, framing, or multi-turn tactics. This under-coverage underestimates real-world risk. We…