Microsoft Technology Licensing, LLC

Timothy William Burrell

Kenneth Dean Johnson

Matthew Ryan Miller

Richard John Black

Manuel Silverio Da Silva Costa

Miguel Oom Temudo De Castro

Craig Henry Wittenberg

Moshe Israel

Jack Wilson Stokes, Iii

Christian Seifert

Hani Hana Neuvirth

Yaakov Garyani

Ely Abramovitch

Gil Lapid Shafriri

Himanshu Chandola

Amir Keren

Various technologies described herein pertain to enforcing control flow integrity by adding instrumentation when source code is compiled or binary code is rewritten. An indirect call to a control transfer target (e.g., in the source code, in the binary code, etc.) can be identified. Moreover, the instrumentation can be inserted prior to the indirect call. The instrumentation can use a bit from a bitmap maintained by a runtime to verify whether the control transfer target is valid. When an executable image that includes the inserted instrumentation runs, execution can be terminated and/or other appropriate actions can be taken when the control transfer target is determined to be invalid; alternatively, execution can continue when the control transfer target is determined to be valid.

Control flow integrity enforcement at scale

Embodiments detect cyberattack campaigns against multiple cloud tenants by analyzing activity data to find sharing anomalies. Data that appears benign in a single tenant's activities may indicate an attack when the same or similar data is also found for additional tenants. Attack detection may depend on activity time frames, on how similar certain activities of different tenants are to one another, on how unusual it is for different tenants to share an activity, and on other factors. Sharing anomaly analysis may utilize hypergeometric probabilities or other statistical measures. Detection avoidance attempts using entity randomization are revealed and thwarted. Authorized vendors may be recognized, mooting anomalousness. Although data from multiple tenants is analyzed together for sharing anomalies while monitoring for attacks, tenant confidentiality and privacy are respected through technical and legal mechanisms. Mitigation is performed in response to an attack indication.

Multitenant sharing anomaly cyberattack campaign detection

Identify a set or session of processes as having certain characteristics. A method obtains a known set or session of processes, wherein the known set or session of processes has the certain characteristics. A set or session of processes to be evaluated is obtained. A weighted similarity measure is performed between the known set or session of processes and the set or session of processes to be evaluated. The weighted similarity measure is performed element wise, where a comparison is performed for each defined element in the set or session of processes to be evaluated against elements in the known set or session of processes.

Large scale malicious process detection

The subject disclosure is directed towards preventing the exploitation by malicious code of object state corruption vulnerabilities, such as use-after-free vulnerabilities. An object class is configured with a secret cookie in a virtual function table of the object, e.g., inserted at compile time. An instrumentation check inserted in the program code evaluates the secret cookie to determine whether the object state has been corrupted before object access (e.g., a call to one of the object's methods) is allowed. If corrupted, access to the object is prevented by the instrumentation check. Another instrumentation check may be used to determine whether the object's virtual table pointer points to a location outside of the module that contains the legitimate virtual function table; if so, object access is prevented.

Using virtual table protections to prevent the exploitation of object corruption vulnerabilities

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Cheltenham, United Kingdom

Timothy William Burrell