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SpyCloud 2026 Identity Threat Report Finds Non-Human Identities Are Now the Leading Path into the Enterprise
SpyCloud survey of 750 security leaders finds compromised non-human identities are the top enterprise entry point, yet only 36% monitor them.
The 2026 Identity Threat Report surveyed 750 cybersecurity leaders at organizations with 500+ employees across North America and Europe. Compromised non-human identities (31%) were the most cited primary attacker entry point, nearly double phishing (17%), while only 36% of organizations monitor AI agents, service accounts and API keys. 68% of respondents reported identity-based events, averaging eight each, and 91% use AI tools but only 56% have formal governance over their privileges.
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Phishing Research Challenges Conventional Security Awareness Testing
Pistachio's 2.47 million phishing simulations across 1,200 organizations show click rates alone mislead, with 30% of IT staff clicking and leak rates more predictive.
Between June 2025 and May 2026, Pistachio sent 2.47 million simulated phishing attempts to more than 123,000 employees at over 1,200 organizations, analyzing click, credential-leak and reporting behavior. Click rates ranged from 26% in Design to 41% in Construction; 30% of tech development and IT staff clicked at least once, while financial services were the most resilient sector. The report argues that click rate alone creates a false sense of security and that combined click, leak and report trends are better resilience indicators.
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Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection
Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.
Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.
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RobResilience: Implementing and Evaluating a Resilience Framework for Cyber-Physical Embodied Systems
RobResilience implements a runtime resilience framework for robots in Webots/ROS2, evaluating tolerable disruption, degradation, and mitigation feasibility across eight attack scenarios.
The paper implements a formal resilience framework for embodied cyber-physical systems using a PR2 robot and ROS2 in a Webots simulation. At runtime it evaluates three predicates — tolerable disruption (δ), tolerable degradation (γ), and mitigation feasibility (μ) — over a compromised device set derived from IDS confidence scores, triggering mitigation strategies when resilience is lost. Eight attack scenarios systematically covering the full predicate state space confirm runtime behavior matches theoretical definitions. The work addresses 'graceful failure paralysis,' where autonomous systems cannot distinguish safe degraded states from catastrophic hazards during attacks.
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Towards Tackling Application Logic Flaws through Autonomous Formal-Logic Modeling and Automated Reasoning
LL-Verifier combines LLMs with logic model checking to automatically discover logic flaws, uncovering vulnerabilities in 27 IoT access-control protocols.
Researchers present LL-Verifier, a framework that uses LLMs to autonomously convert natural-language protocol descriptions and security goals into formal logic models in a new logic language built on Maude, then applies logic model checking for exhaustive verification. The framework targets application-logic flaws that are tied to business semantics and hard to scale with manual analysis. Evaluation on 27 access-control protocols of widely used IoT devices uncovered a range of sophisticated logic vulnerabilities with security and privacy implications.
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