  Clustering algorithms have become a popular tool in computer security to
analyze the behavior of malware variants, identify novel malware families, and
generate signatures for antivirus systems. However, the suitability of
clustering algorithms for security-sensitive settings has been recently
questioned by showing that they can be significantly compromised if an attacker
can exercise some control over the input data. In this paper, we revisit this
problem by focusing on behavioral malware clustering approaches, and
investigate whether and to what extent an attacker may be able to subvert these
approaches through a careful injection of samples with poisoning behavior. To
this end, we present a case study on Malheur, an open-source tool for
behavioral malware clustering. Our experiments not only demonstrate that this
tool is vulnerable to poisoning attacks, but also that it can be significantly
compromised even if the attacker can only inject a very small percentage of
attacks into the input data. As a remedy, we discuss possible countermeasures
and highlight the need for more secure clustering algorithms.
