title: Guardians of the Agentic System: Preventing Many Shots Jailbreak with
    Agentic System
summary: The autonomous AI agents using large language models can create undeniable
  values in all span of the society but they face security threats from
  adversaries that warrants immediate protective solutions because trust and
  safety issues arise. Considering the many-shot jailbreaking and deceptive
  alignment as some of the main advanced attacks, that cannot be mitigated by the
  static guardrails used during the supervised training, points out a crucial
  research priority for real world robustness. The combination of static
  guardrails in dynamic multi-agent system fails to defend against those attacks.
  We intend to enhance security for LLM-based agents through the development of
  new evaluation frameworks which identify and counter threats for safe
  operational deployment. Our work uses three examination methods to detect rogue
  agents through a Reverse Turing Test and analyze deceptive alignment through
  multi-agent simulations and develops an anti-jailbreaking system by testing it
  with GEMINI 1.5 pro and llama-3.3-70B, deepseek r1 models using tool-mediated
  adversarial scenarios. The detection capabilities are strong such as 94\%
  accuracy for GEMINI 1.5 pro yet the system suffers persistent vulnerabilities
  when under long attacks as prompt length increases attack success rates (ASR)
  and diversity metrics become ineffective in prediction while revealing multiple
  complex system faults. The findings demonstrate the necessity of adopting
  flexible security systems based on active monitoring that can be performed by
  the agents themselves together with adaptable interventions by system admin as
  the current models can create vulnerabilities that can lead to the unreliable
  and vulnerable system. So, in our work, we try to address such situations and
  propose a comprehensive framework to counteract the security issues.
paper_authors._id: 67c1b63744d780e60d7c5274
paper_authors.name: Saikat Barua
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-28T13:24:57.086Z
paper_authors._id: 653425f4ed74ace63395826c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/QJlB0DOEel6U9b-95wasK.png
paper_authors.fullname: Saikat Barua
paper_authors.type: user
paper_authors.user: AlignAI
paper_authors._id: 67c1b63744d780e60d7c5275
paper_authors.name: Mostafizur Rahman
paper_authors._id: 67c1b63744d780e60d7c5276
paper_authors.name: Md Jafor Sadek
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T15:21:48.563Z
paper_authors._id: 63c99ab3dfac8071d01b61d4
paper_authors.avatarUrl: /avatars/9151241b8af4d64d7771740587d1b7a5.svg
paper_authors.fullname: MD Jafor Sadek Khan
paper_authors.type: user
paper_authors.user: Jafor
paper_authors._id: 67c1b63744d780e60d7c5277
paper_authors.name: Rafiul Islam
paper_authors._id: 67c1b63744d780e60d7c5278
paper_authors.name: Shehnaz Khaled
paper_authors._id: 67c1b63744d780e60d7c5279
paper_authors.name: Ahmedul Kabir
paper_title: Guardians of the Agentic System: Preventing Many Shots Jailbreak with
    Agentic System
paper_summary: The autonomous AI agents using large language models can create undeniable
  values in all span of the society but they face security threats from
  adversaries that warrants immediate protective solutions because trust and
  safety issues arise. Considering the many-shot jailbreaking and deceptive
  alignment as some of the main advanced attacks, that cannot be mitigated by the
  static guardrails used during the supervised training, points out a crucial
  research priority for real world robustness. The combination of static
  guardrails in dynamic multi-agent system fails to defend against those attacks.
  We intend to enhance security for LLM-based agents through the development of
  new evaluation frameworks which identify and counter threats for safe
  operational deployment. Our work uses three examination methods to detect rogue
  agents through a Reverse Turing Test and analyze deceptive alignment through
  multi-agent simulations and develops an anti-jailbreaking system by testing it
  with GEMINI 1.5 pro and llama-3.3-70B, deepseek r1 models using tool-mediated
  adversarial scenarios. The detection capabilities are strong such as 94\%
  accuracy for GEMINI 1.5 pro yet the system suffers persistent vulnerabilities
  when under long attacks as prompt length increases attack success rates (ASR)
  and diversity metrics become ineffective in prediction while revealing multiple
  complex system faults. The findings demonstrate the necessity of adopting
  flexible security systems based on active monitoring that can be performed by
  the agents themselves together with adaptable interventions by system admin as
  the current models can create vulnerabilities that can lead to the unreliable
  and vulnerable system. So, in our work, we try to address such situations and
  propose a comprehensive framework to counteract the security issues.
paper_ai_keywords: large language models (LLM) many-shot jailbreaking deceptive alignment static guardrails dynamic multi-agent system Reverse Turing Test multi-agent simulations anti-jailbreaking system GEMINI 1.5 pro llama-3.3-70B deepseek r1 models tool-mediated adversarial scenarios prompt length attack success rates (ASR) diversity metrics flexible security systems active monitoring adaptable interventions