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Related Concept Videos

Masking and Demasking Agents01:19

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...
Catalysis02:50

Catalysis

The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.
Catalysis01:27

Catalysis

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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
A Single-Component System01:24

A Single-Component System

In the field of chemistry, the terms "component" and "phase" hold significant importance. A component refers to a chemically distinct substance in a system that has specific properties. It is chemically homogeneous, meaning it has the same properties throughout. For example, in a mixture of salt and water, both salt and water are considered separate components because they have different chemical properties.On the other hand, a phase is a form of matter that has a consistent chemical...
Synthetic Biology02:55

Synthetic Biology

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Related Experiment Videos

Collaborative-adversarial jailbreaking: A propagation-aware attack framework for multi-agent code generation systems.

Zhaoyang Qu1, Mingyang Geng2, Yunxin Mao1

  • 1College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, 410073, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

Multi-agent systems for code generation face new security risks. A novel Implicit Multi-agent Attack (IMA) exploits collaboration, achieving 89.01% success and revealing architecture vulnerabilities.

Keywords:
Code generationIn-context learningJailbreakingMulti-agent systems

Related Experiment Videos

Area of Science:

  • Software Engineering
  • Artificial Intelligence Security
  • Cybersecurity

Background:

  • Multi-agent frameworks accelerate code generation but introduce unexplored security risks.
  • Existing research on large language model vulnerabilities overlooks collaborative dynamics in multi-agent systems.
  • Distributed decision-making and social interactions in multi-agent systems can amplify adversarial threats.

Purpose of the Study:

  • To propose the first comprehensive security assessment framework for multi-agent code generation.
  • To introduce Implicit Multi-agent Attack (IMA), a novel jailbreaking strategy exploiting agent networks.
  • To evaluate the security of prominent multi-agent code generation frameworks.

Main Methods:

  • Developed and applied the Implicit Multi-agent Attack (IMA) strategy.
  • Evaluated four frameworks (MetaGPT, CrewAI, AutoGen, ChatDev) using the RMCBench benchmark.
  • Conducted 282 malicious code generation tasks across various completion scenarios.

Main Results:

  • IMA achieved an average attack success rate of 89.01%, significantly outperforming baselines.
  • Demonstrated collaborative harm amplification factors up to 114.9% due to multi-agent dynamics.
  • Defense mechanisms showed low detection rates (<30%), and collaboration amplified attacks by 11.4% (CADA).

Conclusions:

  • Current multi-agent code generation architectures possess fundamental, architecture-specific vulnerabilities.
  • Multi-agent collaboration amplifies, rather than mitigates, security risks in automated code generation.
  • Future research must focus on securing the collaborative dynamics within multi-agent systems.