From theoretical to operational
In 2024, most AI security threats were proof-of-concept. In 2025, they became tools in attacker toolkits. In 2026, they are operational — we are tracking active campaigns that use AI-specific attack techniques to exfiltrate data, compromise agents, and bypass traditional security controls.
Threat 1: Prompt injection at scale
Automated prompt injection attacks are now commodity — tools that scan enterprise AI surfaces and probe for injection vulnerabilities are available in underground markets. The most dangerous variant is indirect injection via poisoned documents: attackers distribute PDFs and web pages designed to hijack agents that process them.
Threat 2: LLM-assisted social engineering
Spear-phishing campaigns built on LLMs are personalized at a scale that was previously impossible. We are seeing phishing emails that incorporate accurate details about the target's role, recent work, and colleagues — harvested from LinkedIn and company websites, then assembled by an LLM into convincing pretexts.
Threat 3: Model extraction and inversion
Enterprises that fine-tune models on proprietary data face model extraction attacks — adversaries probe the model with carefully crafted inputs to reconstruct training data. We have seen this used to extract PII from models fine-tuned on customer records, with extraction rates of 15-30% of training examples in controlled experiments.
What your 2026 AI security program must cover
At minimum: prompt injection defense on every AI surface, inline DLP on all AI interactions, agent runtime controls for any autonomous system in production, and a threat intelligence feed covering AI-specific attack techniques. This is the baseline — organizations without it are already behind the threat curve.