Contexte et atouts du poste

Deployed AI models on platforms are interesting to at least two different kinds of crowds:
users and attackers. In the first case, it becomes clearer and clearer that the impact of these
models on users' everyday life must be audited for preventing abuse or bias [LMPT24]. In the
second case, the cost of training these models calls for proper defenses against malicious entities
and oensive competitors [MGW]. The ambition of the Cluster SequoIA's FANG chair is
to bridge the gap between these two critical setups: legal auditing and oensive security, in
the domain of modern deployed AI models. From this unique standpoint, and from the body
of work we have contributed to build in the field of AI auditing (e.g., [BGDV, GLMT,
GLMP, Ric26]), we expect to find new insights for attacking and defending deployed AI
models, by finding novel angles.
A key observation from this body of work is that platforms h...