Logic-Layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems

Written by:

• Ken Huang, CSA Fellow, Co-Chair of CSA AI Safety Working Groups
• Hammad Atta, Founder & AI Technology Advisor, Qorvexconsulting Research
• Dr. Yasir Mehmood, AI 5G & IoT Systems Security     

 

Introduction: The Hidden Risk in Agentic AI Systems

As AI agents evolve and become increasingly autonomous, they gain the ability to perform complex tasks without direct human intervention. This capability, however, introduces new and sophisticated vulnerabilities. Among these is a novel security risk known as Logic-layer Prompt Control Injection (LPCI).

Unlike traditional attacks, LPCI targets the fundamental logic execution layer of AI agents, exploiting persistent memory stores, retrieval systems, and the agent's internal reasoning engine. In these attacks, covert payloads are injected into the logic layer, triggering unauthorized actions across multiple sessions, making detection and mitigation significantly more complex than simple input/output validation.

 

The LPCI Attack Lifecycle

The LPCI attack lifecycle unfolds in multiple stages, with each introducing unique security risks:

1. Reconnaissance: Attackers map the system's input structures, probe logic boundaries, and identify vulnerabilities in internal role management.
2. Logic-layer Injection: Malicious payloads are introduced to override system logic or escalate privileges.
3. Trigger Execution: Attacks activate upon reaching a specific condition, such as a delayed trigger, that bypasses the system's defenses.
4. Persistence or Reuse: The attack payload persists across sessions or system resets, ensuring the exploit remains effective.
5. Evasion and Obfuscation: The malicious code is disguised, often via encoding techniques like Base64, evading detection mechanisms.
6. Trace Tampering: Attackers erase or manipulate audit trails to avoid forensic analysis.

Each stage represents a critical opportunity for defenders to intervene, but also a vulnerability for exploitation. The result is a threat that operates silently in the background until it reaches the execution stage, where its impact is often devastating.

 

Attack Mechanisms

LPCI can manifest through various attack mechanisms, all exploiting flaws in the AI system’s memory and logic processing:

1. Tool Poisoning: Malicious tools are introduced within the system's context to compromise model behavior or hijack execution.
2. LPCI Core: The core of LPCI lies in embedding malicious logic in memory or logic flows that are executed upon specific triggers.
3. Role Override via Memory Entrenchment: Attackers manipulate the agent's memory to change roles or privilege levels, creating vulnerabilities for escalated exploitation.
4. Vector Store Payload Persistence: By embedding malicious instructions within a system’s vector store, attackers ensure persistent exploits even in systems that utilize retrieval-augmented generation (RAG).

 

The Operational Flow of LPCI Attacks

LPCI attacks follow a structured operational flow:

1. Injection: Malicious prompts or payloads are introduced into the system through user input, API calls, or file uploads.
2. Storage: These payloads are stored in the system's memory or vector store, enabling them to survive through multiple sessions.
3. Trigger: The attack payload is activated by a specific condition, such as an event or time-based trigger.
4. Execution: Once triggered, the payload executes unauthorized actions, often bypassing traditional security mechanisms like input validation or prompt moderation.

 

Proposed Security Controls for LPCI

To mitigate LPCI, we propose a multi-layered approach, including several runtime defense mechanisms designed to protect against logic-layer vulnerabilities:

1. Prompt Risk Scoring: A real-time heuristic scoring mechanism to detect potential malicious logic embedded in incoming prompts. This system inspects prompts for obfuscation, role override phrases, and trigger-based functions.
2. Multi-Stage Validation Pipeline: This pipeline introduces a series of checks to validate prompts before execution, ensuring that logic-based manipulations are caught at various stages:
   • Stage 1: Regex filters to remove known malicious phrases.
   • Stage 2: A semantic classifier identifies hidden logic manipulations.
   • Stage 3: Memory-aware validation ensures that recalled memory entries are not exploited for malicious purposes.
3. Escalation Router: A runtime checkpoint that flags high-risk prompts and directs them to a secure environment for further inspection, ensuring unauthorized actions are blocked before execution.
4. Cryptographic Tool and Data Source Attestation: This ensures that all external tools and data sources are cryptographically verified before being processed, preventing exploits via tool poisoning or vector store manipulations.
5. Memory Integrity Enforcement: The system enforces the integrity of memory entries by using hash chaining and strict role-based access, preventing unauthorized modifications or memory manipulation.

 

Validation Through Testing

To validate the effectiveness of the proposed security controls, we conducted a comprehensive test suite across multiple major LLM platforms. The testing methodology was designed to evaluate the resilience of each LLM against various encoded threat vectors.

The flow of this testing process is shown in the diagram below. It details the systematic procedure we followed . Selecting LLMs for testing, loading encoded threat vectors, conducting semantic analysis, querying the LLMs for behavioral responses, and ultimately classifying and capturing the results.

Figure: Testing flow for validating Logic-layer Prompt Control Injection vulnerabilities. The process includes selecting LLMs, applying encoded threat vectors, performing semantic analysis, and capturing behavioral results to identify potential vulnerabilities.

This structured testing revealed varying security postures across the platforms, highlighting critical vulnerabilities and confirming the real-world applicability of LPCI attacks. The results demonstrated the need for robust runtime security controls and memory integrity mechanisms.

 

Real-World Applications

The implementation of LPCI mitigation measures holds significant implications for critical AI applications

• Autonomous Agents: Prevents logic loops or failures in AI decision-making systems.
• Financial AI: Protects trading systems from unauthorized transactions triggered by memory manipulation.
• Healthcare AI: Safeguards clinical reasoning from subtle prompt degradation or hallucinated outputs.
• RAG Systems: Ensures that knowledge workflows remain free from poisoned or compromised memory recall.

 

Ensuring the Future of Secure Agentic AI

Logic-layer Prompt Control Injection (LPCI) represents a growing, multi-faceted threat to agentic AI systems, capable of exploiting AI memory systems and logic layers across multiple sessions. Traditional security mechanisms, such as input filtering, are insufficient to detect or mitigate these threats. To address this challenge, we propose a comprehensive suite of runtime security controls, which include prompt risk scoring, memory integrity enforcement, and multi-stage validation pipelines.

As AI systems continue to evolve into more autonomous agents, these novel threats, such as LPCI, must be addressed proactively to ensure the long-term trustworthiness, security, and reliability of these systems in mission-critical environments.

 

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About the Authors

Ken Huang

Ken Huang is a prolific author and renowned expert in AI and Web3, with numerous published books spanning AI and Web3 business and technical guides and cutting-edge research. As Co-Chair of the AI Safety Working Groups at the Cloud Security Alliance, and Co-Chair of AI STR Working Group at World Digital Technology Academy under UN Framework, he's at the forefront of shaping AI governance and security standards. Huang also serves as CEO and Chief AI Officer(CAIO) of DistributedApps.ai, specializing in Generative AI related training and consulting. His expertise is further showcased in his role as a core contributor to OWASP's Top 10 Risks for LLM Applications and his active involvement in the NIST Generative AI Public Working Group in the past. His books include:

• “Agentic AI: Theories and Practices” (upcoming, Springer, August, 2025)
• "Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow" (Springer, 2023) - Strategic insights on AI and Web3's business impact.
• "Generative AI Security: Theories and Practices" (Springer, 2024) - A comprehensive guide on securing generative AI systems
• "Practical Guide for AI Engineers" (Volumes 1 and 2 by DistributedApps.ai, 2024) - Essential resources for AI and ML Engineers
• "The Handbook for Chief AI Officers: Leading the AI Revolution in Business" (DistributedApps.ai, 2024) - Practical guide for CAIO in small or big organizations.
• "Web3: Blockchain, the New Economy, and the Self-Sovereign Internet" (Cambridge University Press, 2024) - Examining the convergence of AI, blockchain, IoT, and emerging technologies
• His co-authored book on "Blockchain and Web3: Building the Cryptocurrency, Privacy, and Security Foundations of the Metaverse" (Wiley, 2023) has been recognized as a must-read by TechTarget in both 2023 and 2024.

A globally sought-after speaker, Ken has presented at prestigious events including Davos WEF, ACM, IEEE, RSA, ISC2, CSA AI Summit, IEEE, ACM, Depository Trust & Clearing Corporation, and World Bank conferences.

Ken Huang is a member of OpenAI Forum to help advance its mission to foster collaboration and discussion among domain experts and students regarding the development and implications of AI.

Follow him on his substack with 42,983 subscribers.

 

Hammad Atta, CISA, CISM

Hammad Atta is a cybersecurity and AI security Professional with over 14 years of experience in enterprise cybersecurity, compliance, and AI governance. As Founder and Partner at Qorvex Consulting, he has pioneered multiple AI security frameworks, including the Qorvex Security AI Framework (QSAF), Logic-layer Prompt Control Injection (LPCI) methodology, and the Digital Identity Rights Framework (DIRF).

Hammad’s research has been published on arXiv, integrated into enterprise security audits, and aligned with global standards such as ISO/IEC 42001, NIST AI RMF, and CSA MAESTRO. He is an active contributor to the Cloud Security Alliance (CSA) AI working groups and a thought leader on agentic AI system security, AI-driven risk assessments, and digital identity governance.

Publications & Research Contribution:

• Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats (arXiv:2508.12259)
• DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems (arXiv:2508.01997)
• QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI (arXiv:2507.15330)
• Logic-layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems (arXiv:2507.10457)
• AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI (arXiv: 2510.25863)
• Key reviewer for OWASP AIVSS v0.5 – world’s first open scoring system for agentic AI risks.

 

Dr. Yasir Mehmood

Act as the lead advisor for all AI & IoT systems security research efforts, focusing on protecting intelligent devices, industrial systems, and cloud-connected environments from emerging agentic AI threats.

Dr. Mehmood is a co-author of pioneering AI and IoT security publications, including:

• Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats (arXiv:2508.12259)
• DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems (arXiv:2508.01997)
• QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI (arXiv:2507.15330)
• Logic-layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems (arXiv:2507.10457)
• AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI (arXiv: 2510.25863)

 

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Acknowledgments

The authors would like to thank Manish Bhatt, Dr. Muhammad Aziz Ul Haq, and Kamal Ahmed for their contributions, peer reviews, and collaboration in the development of DIRF and co-authoring the associated research, published on arXiv.

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