Ryan Pickren (Georgia Institute of Technology), Tohid Shekari (Georgia Institute of Technology), Saman Zonouz (Georgia Institute of Technology), Raheem Beyah (Georgia Institute of Technology)

We present a novel approach to developing programmable logic controller (PLC) malware that proves to be more flexible, resilient, and impactful than current strategies. While previous attacks on PLCs infect either the control logic or firmware portions of PLC computation, our proposed malware exclusively infects the web application hosted by the emerging embedded web servers within the PLCs. This strategy allows the malware to stealthily attack the underlying real-world machinery using the legitimate web application program interfaces (APIs) exposed by the admin portal website. Such attacks include falsifying sensor readings, disabling safety alarms, and manipulating physical actuators. Furthermore, this approach has significant advantages over existing PLC malware techniques (control logic and firmware) such as platform independence, ease-of-deployment, and higher levels of persistence. Our research shows that the emergence of web technology in industrial control environments has introduced new security concerns that are not present in the IT domain or consumer IoT devices. Depending on the industrial process being controlled by the PLC, our attack can potentially cause catastrophic incidents or even loss of life. We verified these claims by performing a Stuxnet-style attack using a prototype implementation of this malware on a widely-used PLC model by exploiting zero-day vulnerabilities that we discovered during our research (CVE-2022-45137, CVE-2022-45138, CVE-2022-45139, and CVE-2022-45140). Our investigation reveals that every major PLC vendor (80% of global market share) produces a PLC that is vulnerable to our proposed attack vector. Lastly, we discuss potential countermeasures and mitigations.

View More Papers

Pisces: Private and Compliable Cryptocurrency Exchange

Ya-Nan Li (The University of Sydney), Tian Qiu (The University of Sydney), Qiang Tang (The University of Sydney)

Read More

dRR: A Decentralized, Scalable, and Auditable Architecture for RPKI...

Yingying Su (Tsinghua university), Dan Li (Tsinghua university), Li Chen (Zhongguancun Laboratory), Qi Li (Tsinghua university), Sitong Ling (Tsinghua University)

Read More

Strengthening Privacy in Robust Federated Learning through Secure Aggregation

Tianyue Chu, Devriş İşler (IMDEA Networks Institute & Universidad Carlos III de Madrid), Nikolaos Laoutaris (IMDEA Networks Institute)

Read More

Analysis of the Effect of the Difference between Japanese...

Rei Yamagishi, Shinya Sasa, and Shota Fujii (Hitachi, Ltd.)

Read More