  Rank-metric code-based cryptography relies on the hardness of decoding a
random linear code in the rank metric. The Rank Support Learning problem (RSL)
is a variant where an attacker has access to N decoding instances whose errors
have the same support and wants to solve one of them. This problem is for
instance used in the Durandal signature scheme. In this paper, we propose an
algebraic attack on RSL which clearly outperforms the previous attacks to solve
this problem. We build upon Bardet et al., Asiacrypt 2020, where similar
techniques are used to solve MinRank and RD. However, our analysis is simpler
and overall our attack relies on very elementary assumptions compared to
standard Gr{\"o}bner bases attacks. In particular, our results show that key
recovery attacks on Durandal are more efficient than was previously thought.
