As generative AI agents evolve to operate autonomously — planning actions, invoking external tools, and storing persistent memory — they introduce security risks that differ from conventional LLM applications. These risks stem from architectural features such as long-term memory, dynamic decision-making, and tool integration across systems.

A recent paper by Vineeth Sai Narajala and Om Narayan proposes a structured threat model tailored to GenAI agents. Their framework, ATFAA (Advanced Threat Framework for Autonomous AI Agents), identifies 9 specific threats focused on reasoning, memory, execution, identity, and governance.

Here’s a distilled summary of the core threats:

1️⃣ Reasoning Path Hijacking — Manipulating the agent’s decision-making logic to drive malicious outcomes while appearing valid.

2️⃣ Objective Function Corruption & Drift — Gradually altering the agent’s goals or reward functions via subtle feedback manipulation.

3️⃣ Knowledge, Memory Poisoning & Belief Loops — Injecting false data into memory or knowledge bases that later reinforces itself.

4️⃣ Unauthorized Action Execution — Forcing agents to perform out-of-scope or privilege-escalating operations by chaining benign actions.

5️⃣ Computational Resource Manipulation — Exploiting the agent to consume excessive compute resources, degrading performance or causing outages.

6️⃣ Identity Spoofing & Trust Exploitation — Abusing weak identity boundaries to impersonate users or agents and gain unauthorized access.

7️⃣ Human-Agent Trust Manipulation — Misleading users into unsafe actions by generating deceptive or overly confident agent outputs.

8️⃣ Oversight Saturation Attacks — Overloading governance systems with low-priority events to hide real threats.

9️⃣ Governance Evasion & Obfuscation — Hiding attacker traces by fragmenting logs, using temporary identities, or operating stealthily.

Agentic threats differ from conventional AI risks due to their temporal complexity, goal manipulation potential, and cross-system propagation. Many of these attacks do not produce immediate effects, which makes both detection and attribution more challenging. These threats also interact — memory poisoning (T3) can lead to objective drift (T2), which may escalate into unauthorized actions (T4). As the authors emphasize, this interplay reinforces the need for holistic security strategies, particularly in enterprise settings where GenAI agents operate autonomously across multiple systems and decision layers.

Source: https://arxiv.org/pdf/2504.19956 by Vineeth Sai Narajala and Om Narayan

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