Guardians of the Agentic System: Preventing Many Shots Jailbreak with
  Agentic System
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.
67c1b63744d780e60d7c5274
Saikat Barua
claimed_verified
2025-02-28T13:24:57.086Z
653425f4ed74ace63395826c
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/QJlB0DOEel6U9b-95wasK.png
Saikat Barua
user
AlignAI
67c1b63744d780e60d7c5275
Mostafizur Rahman
67c1b63744d780e60d7c5276
Md Jafor Sadek
admin_assigned
2025-02-28T15:21:48.563Z
63c99ab3dfac8071d01b61d4
/avatars/9151241b8af4d64d7771740587d1b7a5.svg
MD Jafor Sadek Khan
user
Jafor
67c1b63744d780e60d7c5277
Rafiul Islam
67c1b63744d780e60d7c5278
Shehnaz Khaled
67c1b63744d780e60d7c5279
Ahmedul Kabir
Guardians of the Agentic System: Preventing Many Shots Jailbreak with
  Agentic System
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.
A framework is developed to enhance security in LLM-based autonomous agents through detection and countermeasure strategies for advanced attacks like many-shot jailbreaking and deceptive alignment.
autonomous AI agents large language models security threats many-shot jailbreaking deceptive alignment static guardrails dynamic multi-agent system evaluation frameworks Reverse Turing Test multi-agent simulations tool-mediated adversarial scenarios anti-jailbreaking system prompt length attack success rates diversity metrics active monitoring adaptable interventions