153,747 research outputs found
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start
E-commerce platforms provide their customers with ranked lists of recommended
items matching the customers' preferences. Merchants on e-commerce platforms
would like their items to appear as high as possible in the top-N of these
ranked lists. In this paper, we demonstrate how unscrupulous merchants can
create item images that artificially promote their products, improving their
rankings. Recommender systems that use images to address the cold start problem
are vulnerable to this security risk. We describe a new type of attack,
Adversarial Item Promotion (AIP), that strikes directly at the core of Top-N
recommenders: the ranking mechanism itself. Existing work on adversarial images
in recommender systems investigates the implications of conventional attacks,
which target deep learning classifiers. In contrast, our AIP attacks are
embedding attacks that seek to push features representations in a way that
fools the ranker (not a classifier) and directly lead to item promotion. We
introduce three AIP attacks insider attack, expert attack, and semantic attack,
which are defined with respect to three successively more realistic attack
models. Our experiments evaluate the danger of these attacks when mounted
against three representative visually-aware recommender algorithms in a
framework that uses images to address cold start. We also evaluate two common
defenses against adversarial images in the classification scenario and show
that these simple defenses do not eliminate the danger of AIP attacks. In sum,
we show that using images to address cold start opens recommender systems to
potential threats with clear practical implications. To facilitate future
research, we release an implementation of our attacks and defenses, which
allows reproduction and extension.Comment: Our code is available at https://github.com/liuzrcc/AI
Do not trust me: Using malicious IdPs for analyzing and attacking Single Sign-On
Single Sign-On (SSO) systems simplify login procedures by using an an
Identity Provider (IdP) to issue authentication tokens which can be consumed by
Service Providers (SPs). Traditionally, IdPs are modeled as trusted third
parties. This is reasonable for SSO systems like Kerberos, MS Passport and
SAML, where each SP explicitely specifies which IdP he trusts. However, in open
systems like OpenID and OpenID Connect, each user may set up his own IdP, and a
discovery phase is added to the protocol flow. Thus it is easy for an attacker
to set up its own IdP. In this paper we use a novel approach for analyzing SSO
authentication schemes by introducing a malicious IdP. With this approach we
evaluate one of the most popular and widely deployed SSO protocols - OpenID. We
found four novel attack classes on OpenID, which were not covered by previous
research, and show their applicability to real-life implementations. As a
result, we were able to compromise 11 out of 16 existing OpenID implementations
like Sourceforge, Drupal and ownCloud. We automated discovery of these attacks
in a open source tool OpenID Attacker, which additionally allows fine-granular
testing of all parameters in OpenID implementations. Our research helps to
better understand the message flow in the OpenID protocol, trust assumptions in
the different components of the system, and implementation issues in OpenID
components. It is applicable to other SSO systems like OpenID Connect and SAML.
All OpenID implementations have been informed about their vulnerabilities and
we supported them in fixing the issues
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