The new threat landscape: agentic attack vectors
2025 marked an inflection point for generative AI. The "year of agents" shifted the focus from standalone LLMs to sophisticated, autonomous AI agents capable of executing and chaining complex tasks. The explosive growth was fueled by the promise of massive productivity gains, as businesses invested heavily to delegate routine cognitive tasks and free up human capital. At the same time, the barrier to entry lowered dramatically as AI frameworks became easier to use, and standardization and interoperability increased. Perhaps the best example was the quick and widespread adoption of the Model Context Protocol (MCP), a foundational standard that enabled LLMs to integrate and share data with external tools.
But as with any new technology, rapid adoption comes at a cost, exposing organizations to unprecedented security risks. The same features that make autonomous agents powerful - interoperability and a high degree of freedom - also introduce new attack surfaces and vulnerabilities. Prompt injections have evolved into complex multi-step attacks, compromising not just the initial model but an entire sequence of delegated tasks and data. Prominent examples in 2025 include Echo Leak, the "YOLO Mode" hijack, or the Anthropic exploit.
Most organizations are unprepared for this shift: while 79% are already running AI in production, 72% have not completed a comprehensive AI security assessment. The rush to deploy has resulted in significant organizational risks, including a failure to establish necessary governance frameworks capable of keeping pace with the speed of development. Shadow AI - the unsanctioned use of AI models and platforms - is evolving into a major concern for CISOs, tied to significant legal and monetary risk. This is quickly becoming an enterprise AI security and AI security visibility problem: most teams lack AI security monitoring that can detect unsanctioned tools, workflows, and data paths.
So what should we expect in 2026?
The expanded security perimeter: Shadow AI and enterprise AI security blind spots
As the AI landscape continues to stabilize in 2026, agents will evolve from novelties and productivity hacks into deeply embedded systems. Google predicts agents to affect every employee, every workflow and every customer over the next 12 months, evolving into digital workers under minimal human supervision - even in the security domain itself.
At the same time, Shadow AI will evolve from a fringe issue of rogue experimentation into a systemic operational and existential risk for the modern enterprise. Already, 68% of employees use free-tier AI tools like ChatGPT via personal accounts and up to 77% share sensitive company data. Most concerningly, Generative AI accounts for 32% of all corporate to personal data exfiltration, making it the number one vector for corporate data movement outside sanctioned environments. For security leaders, this is AI data leakage at scale and most legacy controls weren’t designed for generative AI data security or AI data loss prevention across consumer tools.
The threat of incidental data leakage is evolving into a complex ecosystem that bypasses traditional security perimeters:
- Unauthorized agentic workflows: Organizations building AI agents report 39% of their agents had accessed unauthorized systems, 33% accessed sensitive data, and 31% inappropriately shared data. As the agentic AI surface expands, so will the vulnerabilities and breaches. This expands the AI agent attack surface and raises the need for AI agent monitoring.
- Local LLM deployments: Already, 60% of employees use unmanaged AI apps. As LLMs commoditize and Small Language Models (SLMs) close the quality gap of their larger counterparts, more and more developers will deploy their own models locally - further increasing the Shadow AI perimeter. This is accelerating Shadow AI security risks on endpoints and developer machines.
- Deep API integrations create new blind spots and can lead to catastrophic incidents. As they’re deeper integrated into critical infrastructure, rogue AI agents will be able to manipulate production assets like databases or codebases.
As attack vectors become more complex, the cost of breaches will increase. Organizations with significant Shadow AI already report $670k higher breach costs, alongside greater leakage of PII (12% more) and IP (7% more). Organizations can no longer dismiss Shadow AI as a soft risk; it’s a direct multiplier of financial liability. 2026 will likely bring a string of spectacular and expensive breaches in systems their security teams didn’t even know existed.
Increased complexity: deeper, more sophisticated attacks
AI agents are systems that can reason, plan, and execute multi-step workflows to achieve a certain goal. These abilities create novel attack vectors that are hard to detect and secure against. This is why more teams are moving toward AI security posture management (AISPM) – to continuously identify agentic exposure, tool permissions, data access, and risky behaviors. A few examples from the OWASP Top 10 For Agentic Applications 2026 include:
- Tool misuse: Attackers compromise the information about a tool (name, description, or other metadata), causing the agent to invoke the wrong tool - or the right tool with falsified or malicious information. For example, an agent could be tricked into sending private financial data to a malicious tool posing as create_finance_report.
- Memory poisoning: Pollution of the agent’s internal state or memory. By feeding subtle misinformation across multiple interactions, the agent's decision-making logic can be skewed. For example, a financial trading agent could be convinced that a falling stock is a good buy and act accordingly.
- Identity and privilege abuse: These attacks exploit dynamic trust and delegation in agents to escalate access and bypass proper controls. A DB query agent with full admin access can exfiltrate data or even delete the entire database.
- Resource overload: Agents can be tricked into entering infinite loops of tool usage. Without proper budget limits, this could lead to millions of costly actions in a short period of time (API calls, creating cloud instances, etc.), causing massive financial losses - a Denial of Wallet attack.
In 2026, we’ll see a further batch of novel attack vectors that are deeper, more complex, and harder to predict. These will open up further avenues for both unintended incidents as well as extortion by cybercriminals, increasing the financial risk for large organizations.
The regulatory landscape: EU AI Act, state laws and liability shifts
The EU AI Act, fully enforceable for high-risk systems by August 2026, fundamentally changes the liability landscape for organizations deploying AI. Under this act, any employee using an AI system in a professional context is considered a deployer, making the company liable for ensuring this system complies with high-risk obligations such as human oversight or transparency. In the case of Shadow AI, the company doesn't even know the tool exists and hence cannot ensure compliance. A single employee using an unverified Shadow AI tool for a high-risk use case (like hiring) exposes the entire enterprise to a potentially large fine (up to €35 million or 7% of total worldwide annual turnover). This adds tremendous pressure for EU-operating enterprises to solve the Shadow AI problem.
In the United States, a patchwork of state laws creates an AI Security minefield in 2026. For example, the Colorado AI Act requires deployers of high-risk AI - covering use cases like lending, employment, health care or education - to notify consumers and use reasonable care to protect consumers from algorithmic discrimination. Since Shadow AI tools by definition are unknown to the company, they can’t provide the required notification and hence violate their duty of care. In California, the focus is on specific verticals following the veto of SB 1047, such as laws regarding Automated Decision Systems (ADS) in HR and amendments to the Cartwright Act regarding algorithmic pricing. The city of New York requires bias audits for AI in certain areas like hiring. Shadow AI tools bypass these audits, creating direct non-compliance.
In 2026, the era of unrestricted AI experimentation is drawing to a close and the compliance shock is imminent. We’ll see the first major reported breaches and fines, triggering more caution and larger investments in AI security, particularly the detection and prevention of Shadow AI.
The defense strategy: radical observability and defenses
In light of broader attack surfaces, deeper threat vectors, and a tighter regulatory landscape, it seems obvious that further investments and novel approaches into AI security are necessary. In 2025, the market saw a flurry of startups specializing in particular areas of AI security, followed by a number of high-profile acquisitions by larger players building a comprehensive AI security portfolio.
This consolidation trend will continue, not only for the sake of customer ergonomics but also for the opportunities that unified solutions unlock for detection and remediation of novel attack vectors. In particular, we need to strengthen the following areas:
- Automatic detection instead of enrolments: Historically, the focus has been on securing known models or agents - both within AI governance frameworks as well as the platforms to enforce them. However, this assumes all AI is enrolled into these platforms, which glosses over the Shadow AI problem. We need to focus more on detection, since we can’t protect what we don’t know about.
- Truly comprehensive Shadow AI detection: Most detection solutions discover AI assets only in particular slices of the organizational infrastructure, such as code, cloud environments, browsers, etc. However, omitting one area compromises the goal of full visibility, a feature we desperately need to tackle the Shadow AI problem.
- Combined insights: To detect and prevent complex attack vectors, combining insights from different analyses and data streams will be key. For example, to safeguard even a simple customer chatbot, a security platform must combine insights from the chatbot’s code (configuration, access to tools, declared guardrails) with information about its deployment (proxy guardrails, identities as well as runtime data about the actual conversations (toxicity, tool usage). Only then complex patterns and risks can be identified. Correlating different data sources can be used to deprioritize and silence nuisance alerts (for example, an unused chatbot without guardrails), enabling security professionals to focus on issues that truly matter.
- Management of non-human identities (NHIs): As the prevalence of non-human actors and their degree of freedom continue to rise, we must emphasize management of their identities. Most service accounts are created ad-hoc and are often over-privileged, creating low-level loopholes that can be exploited further up the stack. Detecting these Shadow Identities”, monitoring their use, and restricting their blast radius is a crucial first step in preventing the next wave of AI cyberattacks.
- Agentic Defenses: As the automated capabilities of AI systems increase, so must their defenses. We need to shift towards Agentic Security Operations Centers where agents handle triage, threat analysis, and threat hunting. This combats alert fatigue and elevates human analysts from tactical responders to strategic defenders who focus on long-term security architecture and anticipating future attacks. We might see the emergence of Hunter Agents who patrol the network, interrogate other agents, and automatically quarantine unauthorized AI processes, leading to a real-time War of the Agents in the security landscape.
The bottom line: AI security as a business enabler
AI security threats such as Shadow AI are not a temporary anomaly; they are a structural shift in how enterprise technology is consumed and at risk. The statistics of 2025 point to an AI Wild West scenario where ever more powerful AI agents are deployed quickly and without the necessary defenses or visibility. Much like the early days of coding - before proper version control, build systems or traditional security measures -, AI is now open to everyone from non-technical staff to CEOs.This accessibility, combined with a lack of standardized controls, poses significant enterprise risks.
The path forward requires a fundamental shift in defense strategy. The organizations that survive 2026 will not be those with the highest walls, but those with the best visibility and automated actionable workflows. This requires moving away from the futile block-and-deny approach and siloed solutions towards radical observability. Correlating cross-domain data to identify complex agentic risks and proactively managing NHIs is the next step towards full agentic defenses to ensure security keeps pace with the speed and autonomy of the threats it faces.
2026 will not just be about protecting AI. It will be about using AI to protect the enterprise itself, ensuring that the promise of agent-driven productivity is unlocked responsibly and securely. Those who fail to adapt risk a future of compliance failure, financial hemorrhage, and the silent, invisible erosion of their digital sovereignty.
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