Tim Pappa (Walmart)

The evolution of vulnerability markets and disclosure norms has increasingly conditioned vulnerability and vulnerability patching disclosures to audiences. A limited collection of studies in the past two decades has attempted to empirically examine the frequency and the nature of attacks or threat activity related to the type of vulnerability disclosure, generally finding that the frequency of attacks appeared to decrease after disclosure. This presentation proposes extraordinary disclosures of software removal to disrupt collection baselines, suggesting that disclosure of unnamed but topical enterprise software such as enterprise deception software could create a singular, unique period of collection to compare to baseline cyber threat activity. This disruptive collection event could provide cyber threat intelligence teams and SOCs greater visibility into the periodicity and behaviors of known and unknown threat actors targeting them. The extraordinary disclosure of the removal of enterprise software could suggest there are present vulnerabilities on networks, which could prompt increased cyber threat actor attention and focused threat activity, because there is uncertainty about the removal of the software and the replacement of software, depending on the perceived function and capability of that software. This presentation is exploratory, recognizing that there is perhaps anecdotal but generally limited understanding of how cyber threat actors would respond if an organization disclosed the removal of enterprise software to audiences. This presentation proposes an integrated conceptual interpretation of the foundational theoretical frameworks that explain why and how people respond behaviorally to risk and reward and anticipated regret, applied in a context of influencing threat actors with extraordinary disclosures of removal of enterprise software.

View More Papers

Exploring the Influence of Prompts in LLMs for Security-Related...

Weiheng Bai (University of Minnesota), Qiushi Wu (IBM Research), Kefu Wu, Kangjie Lu (University of Minnesota)

Read More

SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Guangke Chen (ShanghaiTech University), Yedi Zhang (National University of Singapore), Fu Song (Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences)

Read More

Towards Real-time Voice Interaction Data Collection Monitoring and Ambient...

Tu Le (University of California, Irvine), Zixin Wang (Zhejiang University), Danny Yuxing Huang (New York University), Yaxing Yao (Virginia Tech), Yuan Tian (University of California, Los Angeles)

Read More

Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

Read More