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Security Threats in Agentic AI System

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arxiv 2410.14728 v1 pith:BP5FF4KT submitted 2024-10-16 cs.CR cs.AIcs.MA

classification cs.CRcs.AIcs.MA
keywords agenticdatasecuritysystemsystemsaccessthreatsvulnerabilities
verification ladder T0 review T1 audit T2 compute T3 formal

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This research paper explores the privacy and security threats posed to an Agentic AI system with direct access to database systems. Such access introduces significant risks, including unauthorized retrieval of sensitive information, potential exploitation of system vulnerabilities, and misuse of personal or confidential data. The complexity of AI systems combined with their ability to process and analyze large volumes of data increases the chances of data leaks or breaches, which could occur unintentionally or through adversarial manipulation. Furthermore, as AI agents evolve with greater autonomy, their capacity to bypass or exploit security measures becomes a growing concern, heightening the need to address these critical vulnerabilities in agentic systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System

    cs.CR 2025-08 conditional novelty 3.0 of 10

    An LLM network-monitoring agent experienced nearly doubled telemetry delays under replayed DoS traffic, and edited memory files led it to choose longer, heavier packet captures, in a two-case test of the MAESTRO threa...

  2. RedTeamLLM: an Agentic AI framework for offensive security

    cs.CR 2025-05 conditional novelty 3.0 of 10

    The paper reports that adding a separate reasoning step to a terminal-operating LLM agent reduces tool calls and improves completion on 4 of 5 entry-level CTF virtual machines, while the framework's memory and plan-co...

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