Agentic AI threats mark a new chapter in cybersecurity where autonomous systems are not just tools, but potential attackers.
Advanced AI agents can reason, plan, and collaborate across systems without human intervention, making them capable of launching complex, multi-stage attacks at machine speed. Defenders face a serious challenge, and traditional security tools struggle to keep up with the dynamic nature of agentic AI.
The way to combat this? Fight AI with AI.
By deploying agentic AI to detect, investigate and respond to threats — often faster and more effectively than human analysts alone — security teams can regain the upper hand.
In this article, we’ll dive into what makes agentic AI threats different, explore ways to prevent agentic AI threats with agentic AI, and examine advanced threats such as memory poisoning, hallucinations, and tool misuse.
What are agentic AI threats?
Agentic AI threats are autonomous AI agents that plan, act, and collaborate —sometimes in unpredictable ways. Unlike traditional software, agentic AI can chain actions and access resources dynamically. With that, it’s essential to have a solid grasp on AI data security.
Some examples of agentic AI threats include:
- LLM agents exfiltrating data across connected tools
- AI assistants with over-permissive access risk leaking sensitive information
- Insider threats or attackers misusing agentic AI credentials
Threat-wise, it’s no longer enough to detect what’s known; organizations must anticipate and respond to what’s possible.
Fighting AI with AI: the strategic imperative
Traditional security tools were designed for static threats and human-driven workflows, entirely different from agentic AI’s dynamic nature.
Traditional tools are unable to inspect AI “reasoning” or track lateral movements, and have an overreliance on identity assumptions like AI using human credentials. Some attacks manipulate the internal mechanics of large language models, leading to sensitive information being silently extracted from agents like Microsoft 365 Copilot.
Unlike basic automation tools, agentic AI can operate autonomously, performing a variety of approved actions independently to accelerate threat detection and response. They’re built to monitor, reason about, and respond to other AI agents, making them a great tool to combat cyber threats.
Using agentic AI to protect against AI threats
Security analysts can use agentic AI as digital assistants that work alongside human analysts, performing critical but time-consuming tasks like:
- Correlating log data across multiple systems to identify patterns
- Associating IP addresses with known threat actors
- Eliminating noise and false positives to focus human attention on genuine threats
- Performing initial triage and investigation to provide analysts with context
- An AI agent flagging another for excessive API calls or abnormal queries
- Auto-triggered policies with an agent strays from its predefined scope
The above statement from our CEO encapsulates a simple philosophy: human expertise remains essential, but it must be augmented by AI to match the scale and sophistication of today’s threats.
This approach is powerful because AI agents continuously improve through active learning. The system becomes increasingly effective over time by analyzing and incorporating analyst feedback, creating an ever-improving security posture.
Using agentic AI to detect agentic AI threats
Consider a scenario where an organization experiences unusual login attempts across multiple cloud services.
First, the system will notice these login attempts across platforms and note their unusual geographic location. Without the need for human intervention, the AI will correlate activity across platforms, cross-reference the IP address against intelligence feeds, examine behavior patterns, and review historical login patterns for the affected accounts.
Now that the agent has a basic understanding of the situation, it will begin to build further context. It will learn that the IP belongs to a VPN service commonly used by threat actors, that the login attempts occurred outside normal business hours, that the login pattern matches known credential stuffing attacks, and that similar activity affected other organizations in the same industry recently.
Instead of simply triggering an alert, the agent will prepare this information for human review. It will compile all relevant evidence, create a timeline of events, prepare recommendations to contain the incident, and prioritize the incident based on the accounts targeted and data potentially exposed.
Finally, the agent will give all investigation information to the human reviewer. For example, a security team member, like a Varonis MDDR analyst, will receive a comprehensive briefing on the situation and can immediately take informed action.
This entire process might take agentic AI only seconds to complete, whereas a human analyst would require significantly more time to gather and analyze the same information.
Advanced agentic AI threats
Not all attacks are created with equal severity. As the technology behind agentic AI advances, so does the complexity of the attacks possible with it.
Memory poisoning attacks
Memory is crucial for agentic AI to function effectively. It includes short-term states, long-term knowledge, and external resources. While memory makes AI powerful for productivity, it also becomes a vulnerable asset to organizations.
Memory poisoning occurs when attackers manipulate what the AI remembers. Attacks start with crafted emails that insert malicious commands into an agent’s memory. The agents then execute harmful actions, believing them to be normal. These actions, like data sharing or financial approvals, can be detrimental if used maliciously. Persistent memory will then spread poisoned beliefs across users and tasks. Without any defense, the risk of data leakage is significant.
You can defend against memory poisoning by isolating sessions, tracking provenance of your data, automating anomaly detection and rollback, and multi-agent consensus validation to ensure if one agent follows through with bad behavior, others won’t follow suit.
Tool misuse and privilege escalation
Agentic AI often invokes tools like APIs, file systems, and databases. This autonomy risks agents being manipulated through poisoned input or memory. Indirect prompt injection can also trigger unintended tool use. Finally, excessive permissions can lead to “confused deputy” vulnerabilities.
Defending against tool misuse and privilege escalation means your organization should have strict least privilege enforcement, input and output validation, immutable logging and sandboxing, and rate limiting and anomaly detection.
Hallucination attacks
Plausible but false outputs from agentic AI, or hallucinations, can be recorded in memory and be reinforced and spread across agents.
For example, an AI can absorb a fake policy and use it to shape its decision making, like an agent at a healthcare provider could recommend inaccurate treatments based on reinforced false data.
Countermeasures to limit hallucinations can include grounding AI outputs with trusted data, implementing memory versioning and rollback, cross-agent consensus for memory and decisions, and implementing forensic analysis of hallucinated decisions.
Practical implementation for preventing agentic AI threats
For organizations considering how to approach agentic AI for security purposes, several considerations are important:
Integrate with existing security infrastructure: Agentic AI solutions should complement and enhance existing security tools rather than replace them entirely.
Establish clear handoff protocols: Well-defined processes are key to success when AI hands off incidents to human analysts to avoid gaps in response.
Continuous learning mechanisms: AI systems should be able to learn from analyst actions and receive feedback to improve over time.
Appropriate autonomy boundaries: Clearly define what actions AI agents can take independently versus what require human approval.
Explainability: The system should provide clear explanations for its findings and recommendations to build trust with human analysts.
The future of agentic AI in security
As the technology behind agentic AI continues to evolve, we can expect to see even more sophisticated applications of agentic AI in security, such as:
- Cross-platform threat correlation that identifies far-reaching attack patterns
- Predictive threat intelligence that forecasts potential attack vectors based on behavior
- Automated remediation, where approved containment actions are taken without human intervention in critical scenarios
- Adaptive defense postures that dynamically adjust based on the current threat landscape
The Varonis MDDR advantage: Human expertise amplified by AI
Varonis Managed Data Detection and Response (MDDR) [MDDR PAGE] brings together award-winning threat detection, a global team of elite security experts, and the power of agentic AI to protect your most sensitive data, 24x7x365.
By amplifying human expertise with autonomous, AI-driven detection and response, Varonis offers a new standard in cyber defense tailored for the era of agentic AI threats.
Industry-leading SLAs
Varonis meets the high standards organizations expect from their data security platform. It responds in 30 minutes to ransomware and 120 minutes for all other threats, all enabled by AI-enhanced speed and triage.
Instant time-to-value
Gone are the days of months-long or even days-long implementation. Varonis deploys in hours, giving your organization protection of your environment without waiting too long.
Round-the-clock coverage
Varonis operates 24x7x365, offering organizations continuous monitoring without fatigue, blind spots, or attention lapses.
Proactive threat hunting
Varonis stops threats before they escalate with regular posture assessments and AI-powered detection to surface threats and alert your security team immediately. Sensitive data is being surfaced, shared, and accessed in new ways — often without security teams knowing.
Seamless integration
Alongside Varonis’ ability to protect the largest and most important data stores and applications across the cloud, it works in tandem with your security team and escalates only when needed.
The nature of cyberattacks is changing in the age of AI. Sensitive data is being surfaced, shared, and accessed in new ways — often without security teams knowing. Get started with a free AI Data Risk Assessment to discover how Varonis can help your organization adopt AI.
Frequently Asked Questions (FAQs)
What are agentic AI threats?
Agentic AI threats refer to risks posed by autonomous AI agents that can independently plan, take actions, and collaborate across systems, potentially leading to data exfiltration, privilege escalation, or operational disruption without direct human involvement. These agents can exploit access and adapt dynamically, making them more dangerous than static, rule-based automation.
How are agentic AI threats different from traditional cyber threats?
Unlike traditional threats, which often follow predictable patterns or rely on human actors, agentic AI threats involve intelligent agents capable of chaining multiple actions, reasoning in real time, and navigating complex environments. This enables them to bypass static defenses and exploit systems at machine speed.
Can traditional security tools detect agentic AI threats?
Most traditional tools struggle to detect agentic AI threats because they rely on static rules, signature-based detection, or simplistic behavioral baselining. Agentic AI operates in more nuanced and dynamic ways, requiring intelligent, adaptive detection methods that can keep up with its decision-making and lateral movements.
What is memory poisoning?
A method where attackers inject false data into an AI's memory, influencing decisions over time.
What is hallucination in agentic AI?
When AI generates false but plausible information that influences tools, memory, or actions.
What is tool misuse?
When AI agents use APIs or integrated systems for unauthorized actions within their permission scope.
How can organizations defend against agentic AI threats?
To defend against agentic AI threats, organizations must adopt an "AI vs. AI" strategy, deploying autonomous AI agents to detect, analyze, and respond to suspicious activity in real time. Solutions like Varonis MDDR combine human expertise with agentic AI to monitor behaviors, flag anomalies, and respond before damage occurs.
Why is it important to act now on agentic AI threats?
Agentic AI threats are quickly becoming mainstream as attackers incorporate AI into their arsenals. The longer organizations wait to modernize their defenses, the greater the risk of being blindsided by fast-moving, intelligent threats. Implementing agentic AI for defense today means building resilience for tomorrow.
