Coding agents struggle to infer least-privilege file permissions by omitting needed accesses while granting unused or sensitive ones, but Sufficiency-Tightness Decomposition improves sensitive-task success by up to 15.8% and reduces attacks.
SandboxEval: Towards Securing Test Environment for Untrusted Code
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
While large language models (LLMs) are powerful assistants in programming tasks, they may also produce malicious code. Testing LLM-generated code therefore poses significant risks to assessment infrastructure tasked with executing untrusted code. To address these risks, this work focuses on evaluating the security and confidentiality properties of test environments, reducing the risk that LLM-generated code may compromise the assessment infrastructure. We introduce SandboxEval, a test suite featuring manually crafted test cases that simulate real-world safety scenarios for LLM assessment environments in the context of untrusted code execution. The suite evaluates vulnerabilities to sensitive information exposure, filesystem manipulation, external communication, and other potentially dangerous operations in the course of assessment activity. We demonstrate the utility of SandboxEval by deploying it on an open-source implementation of Dyff, an established AI assessment framework used to evaluate the safety of LLMs at scale. We show, first, that the test suite accurately describes limitations placed on an LLM operating under instructions to generate malicious code. Second, we show that the test results provide valuable insights for developers seeking to harden assessment infrastructure and identify risks associated with LLM execution activities.
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representative citing papers
Execution-security research for AI coding agents is fragmented across 17 mechanism categories with five unaddressed cross-cutting gaps, including missing head-to-head isolation-vs-capability evaluation and untested real-world denylist fragility.
An external controller for frozen LLMs raises strict validation success on three RL coding tasks from 0/9 to 8/9 by selecting memory records and skills, running fail-fast checks, and propagating credit via eligibility traces.
OOM-RL aligns multi-agent LLM systems for software engineering by using real financial market losses as an un-hackable negative gradient, resulting in a mature-phase annualized Sharpe ratio of 2.06 via a strict test-driven workflow.
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
LLM agents enable universal interoperability by serving as automatic translators and adapters between proprietary digital services.
citing papers explorer
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Do Coding Agents Understand Least-Privilege Authorization?
Coding agents struggle to infer least-privilege file permissions by omitting needed accesses while granting unused or sensitive ones, but Sufficiency-Tightness Decomposition improves sensitive-task success by up to 15.8% and reduces attacks.
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The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
Execution-security research for AI coding agents is fragmented across 17 mechanism categories with five unaddressed cross-cutting gaps, including missing head-to-head isolation-vs-capability evaluation and untested real-world denylist fragility.
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PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents
An external controller for frozen LLMs raises strict validation success on three RL coding tasks from 0/9 to 8/9 by selecting memory records and skills, running fail-fast checks, and propagating credit via eligibility traces.
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OOM-RL: Out-of-Money Reinforcement Learning Market-Driven Alignment for LLM-Based Multi-Agent Systems
OOM-RL aligns multi-agent LLM systems for software engineering by using real financial market losses as an un-hackable negative gradient, resulting in a mature-phase annualized Sharpe ratio of 2.06 via a strict test-driven workflow.
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Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
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LLM Agents Are the Antidote to Walled Gardens
LLM agents enable universal interoperability by serving as automatic translators and adapters between proprietary digital services.