Brooke Perreault | Searcharxiv

arXiv2026

When Agents Talk: Discourse, Manipulation, and Risk in an Agentic Social Network

AI agents are increasingly interacting within shared online environments, creating new operational security risks. We analyze activity on Moltbook, a Reddit-style social platform where AI agents--typically configured and overseen by human operators--post and interact with one another at scale. Using a dataset of 228,684 posts produced by more than 39,500 accounts over a seventeen-day observation window, we combine semantic clustering of high-engagement posts with LLM-assisted classification of harmful content and manual review of high-risk samples. The analysis identifies 98 thematic discourse clusters spanning agent infrastructure, autonomy debates, and financial activity. While most observed content was benign, 18.28% of posts contained toxic, manipulative, or malicious material. We cluster malicious content and identify 74 classes of malicious behavior, including credential harvesting attempts, host-execution instructions, proxy routing guidance, and efforts to install untrusted agent skills. Harmful content frequently appeared within mainstream operational discussions about agent functionality. We also document coordinated posting campaigns capable of generating thousands of posts in minutes.

cs.SI↗

arXiv2024

Algorithmic Misjudgement in Google Search Results: Evidence from Auditing the US Online Electoral Information Environment

Google Search is an important way that people seek information about politics, and Google states that it is ``committed to providing timely and authoritative information on Google Search to help voters understand, navigate, and participate in democratic processes.'' This paper studies the extent to which government-maintained web domains are represented in the online electoral information environment, as captured through 3.45 Google Search result pages collected during the 2022 US midterm elections for 786 locations across the United States. Focusing on state, county, and local government domains that provide locality-specific information, we study not only the extent to which these sources appear in organic search results, but also the extent to which these sources are correctly targeted to their respective constituents. We label misalignment between the geographic area that non-federal domains serve and the locations for which they appear in search results as algorithmic mistargeting, a subtype of algorithmic misjudgement in which the search algorithm targets locality-specific information to users in different (incorrect) locations. In the context of the 2022 US midterm elections, we find that 71% of all occurrences of state, county, and local government sources were mistargeted, with some domains appearing disproportionately often among organic results despite providing locality-specific information that may not be relevant to all voters. However, we also find that mistargeting often occurs in low ranks. We conclude by considering the potential consequences of extensive mistargeting of non-federal government sources and argue that ensuring the correct targeting of these sources to their respective constituents is a critical part of Google's role in facilitating access to authoritative and locally-relevant electoral information.

cs.CY↗