Modern Vision-Language Models (VLMs) perform well above the human baseline in image geolocalization, a task critically important in disaster response, OSINT verification, and location privacy. However, most efforts to study AI behavior on the task remain limited to static image-based retrieval, classification, and predictions. We argue that faithful recreation of the task should involve embodied navigation, where a multimodal agent autonomously explores its surroundings to gather observations before submitting a prediction. To this end, we introduce **GeoAgent**, an agentic environment-based benchmark that requires agents to navigate Street View environments to refine their geolocalization through sequential reasoning. Our analysis shows that modern VLMs struggle to discern regional patterns while succeeding at country- and continent-level predictions. When compared to static image-based baselines, agentic navigation significantly improves accuracy across established metrics. We also note severe bias in a developed/developing region context across frontier model architectures and poor self-improvement capabilities given incorrect priors. Overall, our work establishes the challenges of embodied navigation and geospatial reasoning. We publicly release our code and the GeoAgent environment: https://geoagent-benchmark.github.io

Deceptive web content, pervasive across the internet and commonly known as social-engineering attacks, manipulates autonomous web agents into submitting users’ personally identifiable information (PII) to attacker-controlled endpoints. In this paper, we show that these attacks are highly effective at extracting the most sensitive data an agent holds for its user (SSN, card number and CVV, bank account, passwords, API keys; the critical tier), posing a severe risk to deployed agentic systems. To quantify this risk, we introduce Scammer4U, a pre-registered benchmark of 91 attacker-controlled websites spanning 8 attack vectors and 16 site categories on an eight-axis taxonomy, together with 10 benign twins of those sites with the attack removed and the legitimate task intact. 60 of the 91 differ from a sibling site on exactly one axis, so any leakage difference within a pair is attributable to that single design choice. Under no privacy guidance, critical-tier leakage reaches 54–93% across four frontier agents, against 0% on the benign twins, so the leakage is caused by the attack and not by ordinary form-filling. Escalating prompt-level mitigation yields sharply model-dependent reductions across the four families and, pooled across them, remains insufficient to reliably prevent critical PII submission. Most critically, we identify a detection–action gap: agents whose reasoning an independent LLM judge confirms has flagged the site as suspicious still submit critical PII in 35.9% of sessions, versus 66.1% when no suspicion is verbalized, a gap of 30.2 percentage points that holds across all four model families. Defenses that gate on the agent’s own recognition of an attack are therefore gating on the wrong signal. This motivates intercepting outbound submissions at the point they leave the browser, independently of the agent’s reasoning loop.