AI & ML
GreyZone: Production-quality Agentic Application Threats
arXiv cs.SE0 views
arXiv:2611.09161v1 Announce Type: new Abstract: Agents are increasingly deployed across domains such as coding, banking, and commerce. Their ability to invoke tools and change application state makes security testing important for reliable deployment. Although agent security has been studied through various adversarial testing approaches, benchmarks grounded in production agent environments remain limited. We introduce GreyZone, a benchmark for evaluating automated red-teaming services against real-world agent applications. Unlike benchmarks such as AgentDojo, which primarily evaluate subject agents under attack, GreyZone evaluates services' ability to generate and deliver attacks that cause verified security-policy violations. We select independently developed, open-source agent applications with security boundaries concerning access permissions, confidentiality, and action authorization. These applications expose diverse attack surfaces, including those arising from multi-agent interactions and multimodal inputs. By grounding evaluation in application environments that preserve production-relevant workflows and independently verifying outcomes, GreyZone supports research on automated agent security testing. Specifically, we present two preliminary case studies using Promptfoo on Letta Code and Clawith to examine attack delivery, agent behavior, and verified outcomes. We release the benchmark, including subject agents and attack surfaces for each application, to support further research on agent testing methods.
Source:https://arxiv.org/abs/2611.09161PreviousWhen Routine Operations Break Architectural Pattern Relationships in LLM Agent HarnessesNextOn the Limits of Execution Traces for Repairing Assertion Failures in LLM-Generated Code
Related posts
Nvidia investigating reports that CEO Jensen Huang was targeted by Chinese intelligence
0Phys.org Tech
Under fire over AI, effective altruism wants to tell its own story
0Phys.org Tech

'I am deeply sorry': Jensen Huang affair with suspected Chinese spy sees Nvidia open investigation into its CEO — but what happens next?
0TechRadar
What free AI training in Nepal can teach you before you pay for a class
0dev.to

Agents draft. People decide.
0dev.to UX

โค้ดเพิ่ม 400% แต่คุณไม่ได้เร็วขึ้น 400%: GitHub ชี้คอขวดใหม่อยู่ที่การรีวิว
0dev.to GitHub