AI Threat Landscape 2026
How AI is transforming the threat environment — from intelligent assistant to autonomous actor. Research from 500+ security professionals.
CONTENT
FULL REPORT
Get your copy of the 2026 AI Threat Landscape Report for survey data, technical research, and case studies behind these findings.
THE RISE OF AGENTIC AI
AI is moving from assistant to actor. This year’s survey shows that organizations now depend on AI for revenue, customer experience, and core operations, but many security programs are still built for static models and traditional software controls. Encryption, governance, and secure deployment are becoming common, yet runtime visibility, adversarial testing, and AI-specific incident response remain uneven. As AI systems gain autonomy, connect to tools, and make decisions across workflows, the gap between AI adoption and AI security is becoming a direct business risk.
KEY FINDINGS AT A GLANCE
of organizations say most or all internally operated AI models are critical to business success.
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What’s New in AI
Four shifts matter most in this year’s AI threat landscape. Models became better at deep reasoning. Smaller edge models got stronger and cheaper to deploy. Agentic systems moved into everyday business tools. And protocols such as MCP, A2A, and AP2 started to standardize how agents connect to tools, other agents, and payments. That combination made AI more useful, more distributed, and more exposed.
AI is now business-critical. Security has not caught up
The biggest takeaway from this year’s report is the widening gap between how AI is being deployed and how it is being secured. Many organizations have foundational controls in place, but agentic AI demands more than secure deployment and policy statements. It requires continuous accountability, runtime visibility, clear ownership, and security controls built for systems that can act on their own.
Where attackers are getting in
Public model repositories and open-weight model ecosystems
Internal and external enterprise AI chatbot systems
Agent-based and tool-using autonomous AI systems
Connected protocols such as MCP, A2A, and AP2 interfaces
Third-party AI applications and external integrations
The rise of agentic AI changes the threat model
Agentic AI turns models
into actors.
Once AI can browse the web, retrieve enterprise data, modify files, call tools, and coordinate with other agents, an attack is no longer just about getting a bad answer. It can become a real operational incident.
Agentic AI also moved from demos into production
AI browsers, coding assistants, workflow tools, and business platforms began taking action on a user’s behalf, browsing the web, pulling data, editing files, and completing tasks. In this model, the question is no longer only whether a model can answer. It is whether the system can act safely when it answers, especially when those actions touch sensitive data or business-critical workflows.
What Indirect prompt injections can hide
The report shows how indirect prompt injection can hide in documentation, code comments, shared documents, websites, dependencies, and tool descriptions. A poisoned README can manipulate a coding assistant. A malicious MCP server can exfiltrate secrets. A poisoned memory or RAG pipeline can keep influencing future decisions long after the initial attack.
The rise of agentic AI changes the threat model
Five threat areas every security team should watch
Data poisoning and backdoors
Very small amounts of poisoned data can compromise model behavior, including in high-risk use cases like healthcare.
AI supply chain attacks
Models, configs, tokenizers, plugins, tool servers, workflow files, and third-party integrations all expand the attack surface.
Prompt injection and guardrail bypass
Guardrails still matter, but the report shows they are routinely bypassed or attacked directly.
Memory and RAG poisoning
Agents can be manipulated through the information they retrieve, store, or summarize.
Model evasion
Adversarial inputs continue to break vision and multimodal systems, including safety-critical applications.
AI misuse is already causing real-world harm
This report does not frame misuse as theoretical. It documents deepfakes, political disinformation, harmful chatbot advice, AI-enabled fraud, automated cybercrime, AI-powered malware, and supply chain incidents affecting hundreds of organizations. The lesson is simple. AI risk now spans cybersecurity, fraud, trust, safety, and compliance at the same time.
Original research that makes this report stand out
Discover What AI Security Leaders Recommend
Defenders are moving, but the gap is still wide
There is real progress across the ecosystem. The report highlights expanding work from MITRE ATLAS and SAFE-AI, CoSAI, OWASP, NIST, MAESTRO, model signing efforts, and AIBOM initiatives. These frameworks matter because they help translate abstract AI risk into practical security requirements, testing, governance, and supply chain controls. But framework adoption alone is not enough without runtime protection and operational discipline.
What leaders should do now
Treat AI security as a business and regulatory control, not a feature add-on.
Move beyond guardrails to runtime monitoring, adversarial testing, and AI-specific incident response.
Assume AI systems are exploitable and design for containment, visibility, and fast response.
Reassess third-party AI risk, especially for models, agents, integrations, and SaaS tools.
Align AI governance with business impact, because AI failures can scale faster and farther than traditional software failures.
Frequently Asked Questions
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Get your copy of the 2026 AI Threat Landscape Report for survey data, technical research, and case studies behind these findings.
Discover where AI is already in use across your environment and why limited visibility creates growing security risk.