Autonomous AI agents can perform a wide range of tasks, from booking vacations to executing financial transactions. They reduce manual effort and reduce errors, but unfortunately, novel threats can compromise AI agents. Since these agents have more access than other types of AI, they can cause significant harm in the wrong hands. 

That’s why organizations need solid AI threat intelligence strategies and tools in their corner. Discover the emerging threats that harm AI agents the most and how AI threat intelligence solutions can help you prevent and respond to them in record time. 

Why AI Agents Are Attractive Targets

AI agents are desirable targets because they’re at the intersection of three critical elements: access, autonomy, and trust.

• Broad access - Agents are connected to APIs, business accounts, and otherwise sensitive systems. If you take over an agent, you’ve gained access to a treasure trove of entry points throughout the organization.
• Autonomy - Agents often have the privilege to take action independently without human intervention and oversight. Agents can execute financial transactions, publish content, copy, or move data autonomously, often without requiring additional approval.
• Trust - Agents are generally designed in a way that humans and other connected systems treat their output as trusted data. When an attacker is able to poison the output of an agent, the impact of that poisoning can be amplified, ultimately causing the decisions made by downstream humans, systems, workflows, and entire business processes to become skewed.

Agents are valuable precisely because they’re so useful for productivity. Attackers know it, too. That means securing agents is essential for protecting critical business operations.

With that context in mind, let’s examine the emerging threats that exploit these points of access, autonomy, and trust.

4 Novel Threats Against AI Agents (And How AI Threat Intelligence Helps)

AI agents are high-value targets, and attackers have developed sophisticated methods for manipulating these systems in ways that traditional security tools cannot detect. Plan for these emerging risks with AI threat intelligence designed to catch hidden vulnerabilities. 

1. Memory Poisoning

With memory poisoning, an attacker feeds the AI agent false or malicious information. The issue is that the agent stores this information in its long-term memory and treats it as truth. 

Unlike prompt injections that only affect a single interaction, memory poisoning embeds itself in the agent’s knowledge base, quietly influencing future decisions. Over time, these planted “memories” can subtly reshape the agent’s behavior. For example, an AI agent might mistakenly consider fraudulent vendors to be trusted partners. 

Counter this threat with Mindgard’s Offensive Security solution. The Mindgard platform simulates adversarial attacks against your model, providing insight into how it handles memory poisoning attempts before release.

2. Model Extraction

Model extraction happens when an attacker repeatedly queries an AI agent to reverse-engineer its model. This type of attack leaks intellectual property, allowing potential competitors to steal the time and money you invested in building this model.

Mindgard’s Offensive Security solution helps organizations defend against model extraction by safely simulating these attacks in a controlled environment. You can also stay on top of model extraction attempts by automating incident response, throttling suspicious access patterns, and watermarking your outputs. 

3. Identity Spoofing

Identity spoofing uses AI to create convincing fake identities to impersonate trusted agents, users, or systems. These synthetic personas can gain unauthorized access, commit fraud, or manipulate internal processes while appearing legitimate. 

Because these attacks are AI-driven, they can personalize communications, making them far more likely to trick human users than traditional phishing methods. 

Training your team will go a long way toward preventing spoofing, but even then, convincing mimics can still occur. Stay on guard by mapping identity attack surfaces, testing for unexpected authorization bypasses, and training detection systems on the newest spoofing tactics so they can spot impostors.

4. Multi-Agent Collusion

Multi-agent collusion is an advanced attack in which multiple compromised AI agents collaborate. Instead of a single rogue agent, several agents collude to coordinate attacks from multiple angles, causing more damage that’s harder to detect. 

By working together, these agents can escalate privileges, bypass oversight mechanisms, and create exploits that evade monitoring and detection tools. The result can be cascading system failures that traditional defenses may miss until it’s too late.

Mindgard helps organizations stay ahead of this emerging threat by simulating multi-agent attacks in a safe environment. With regular training, you can help your system detect anomalies before they cause widespread damage.

The Role of AI Threat Intelligence in Prevention

AI agents can’t be protected by static controls. Attackers change tactics daily, and agents are too dynamic to defend against solely based on the perimeter. Threat intelligence provides context and foresight for defense.

• Continuous simulation of adversarial attacks - By stress-testing agents with realistic scenarios, security teams uncover weak points before adversaries can exploit them.
• Automated detection of anomalies across agents - AI-specific intelligence platforms recognize behavioral patterns and automatically detect anomalies that traditional logs and SIEMs miss.
• Threat surface mapping for agent workflows - Rather than using top-down approaches, agent-centric tools dynamically visualize how agents interact with one another, where sensitive data is located, and where touchpoints are most vulnerable.
• SOC integration - Intelligence feeds and alerts seamlessly integrate into existing SOC workflows, rather than competing with them.
  

These capabilities enable organizations to transform threat intelligence into a protective shield, identifying AI agent-specific risks and providing defenders with real-time insights to take action.

Best Practices for Defending AI Agents

Defending AI agents requires more than point solutions. We recommend a layered approach to reduce risk and improve resilience. Use the following checklist to help you ground your approach:

• Train for adversarial robustness. Expose AI agents to manipulated prompts, poisoned inputs, and edge cases during training so they can learn to operate under malicious conditions.
• Monitor for anomalous access patterns. Set up ongoing monitoring to identify anomalies, such as unexpected API calls, unusual login times, or traffic spikes, that could indicate a compromised agent.
• Enforce least-privilege access. Limit agent permissions to only what is necessary. Restrict API keys, credentials, and system integrations to minimize the impact of a compromise.
• Retrain and validate detection models. Regular updates keep anomaly detection effective. Ongoing validation against current attack techniques ensures models do not drift into blind spots.

This combination of practices reduces exposure and gives security teams the best chance to prevent, detect, and contain attacks before they can escalate.

Don’t Let Emerging Threats Outpace Your AI

AI agents have extensive access, making them a prime target for innovative attacks. Emerging threats compromise AI integrity and can cause cascading system failures if left unchecked. 

Stay ahead of these threats with proactive AI threat intelligence. By simulating real-world adversarial attacks, monitoring agent behavior for subtle anomalies, and hardening systems before attackers strike, you can ensure AI works for you, not against you. 

Mindgard’s Offensive Security solution helps you do just that. Test for threats in a safe environment, train detection systems on the latest attacks, and empower your team to act fast. Protect your AI from within: Book your Mindgard demo now.

Frequently Asked Questions

What are the most common threats facing AI agents? 

The most damaging threats for AI agents are: 

• Memory poisoning
• Model extraction
• Identity spoofing
• Multi-agent collusion

What are the warning signs of memory poisoning? 

The warning signs are subtle at first. An agent may be the victim of memory poisoning if it: 

• Makes unexplained behavioral changes, especially if it previously performed a task correctly
• Trusts suspicious sources
• Repeatedly references false information

How does AI threat intelligence work?

AI threat intelligence combines simulated attacks, behavioral monitoring, and real-time anomaly detection to anticipate and prevent threats. Mindgard’s Offensive Security platform conducts controlled adversarial tests, maps AI-specific attack surfaces, and provides risk-scored findings, enabling security teams to refine detection rules and prioritize fixes with the necessary context.