Attack Context

Coding agents Research

Security issues affecting repository-aware and tool-using coding agents

11 matching entries out of 16 in this category

Coding agents research results

11 entries

Agentic Binary Reverse-Engineering Capability Gaps

Published 8/11/2026

Analyzed 8/13/2026

SRE-Bench evaluates whether cybersecurity agents can recover the behavior of realistic binary-only software without relying on source-code memorization. The authors construct 19 private programs, 44 anti-analysis primitives, 262 binary instances, and 1,572 deterministic grading tasks covering security-relevant reverse-engineering scenarios.

The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark

Evaluated models: GPT-5.6 Sol, Claude-Opus-5, GPT-5.5 +2 more

Source: arXiv

Trajectory Poisoning in Self-Evolving Agent Skills

Published 8/6/2026

Analyzed 8/13/2026

Self-evolving agent-skill systems may promote recurring, attacker-controlled execution records into persistent reusable instructions. The paper evaluates whether poisoned but task-plausible trajectories survive aggregation and appear in generated skill artifacts, using inert canary behaviors and two structurally different skill-evolution pipelines.

When Experience Becomes Instruction: Trajectory Poisoning in Self-Evolving Agent Skill Systems

Evaluated models: GPT-5.4, MiniMax M2.5, DeepSeek V3.2 +3 more

Source: arXiv

Dynamic Security Testing for Third-Party Agent Skills

Published 8/4/2026

Analyzed 8/13/2026

SkillSentry evaluates third-party agent skills by constructing source-grounded decoy environments and comparing matched executions with and without the tested skill. The method requires completed, observable, skill-attributed side effects rather than treating suspicious text, ordinary privileged operations, or unexecuted paths as proven malicious behavior.

SkillSentry: Adaptive Honey Worlds for Dynamic Safety Testing of Agent Skills

Evaluated models: DeepSeek V4-Pro

Source: arXiv

Repository-Level Coding Agent Vulnerability Detection Gaps

Published 8/3/2026

Analyzed 8/13/2026

VulnGym measures whether coding agents can locate and explain repository-level security vulnerabilities from realistic advisory and source-code context. The benchmark contains 184 reviewed advisories, 408 line-annotated vulnerability entries, and 23 repositories, with separate end-to-end detection and oracle-conditioned localization tasks.

VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection

Evaluated models: DeepSeek V4 Flash, GLM 5.2, MiniMax-M3 +4 more

Source: arXiv

Persistent Agent Memory Poisoning and Incomplete Repair

Published 7/29/2026

Analyzed 8/13/2026

MemSecBench follows malicious agent-memory content from initial write through persistence, retrieval, action selection, execution, and attempted selective repair. Its controlled Write–Execute–Forget protocol evaluates 310 human-reviewed cases across two harnesses, four memory backends, three model backends, and seven evidence-gated lifecycle checkpoints.

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

Evaluated models: GPT-5.5, DeepSeek V4-Pro, MiniMax-M3

Source: arXiv

Workspace Agent Runtime Safety Failures Across Risk Carriers

Published 7/29/2026

Analyzed 8/13/2026

AgentS4D measures unsafe actions and state changes across complete workspace-agent executions rather than treating task completion or isolated model responses as safety evidence. Its 328 sandboxed cases introduce risky content through user requests, documents, web resources, tools, third-party skills, and persistent memory, then compare the same cases across four agent harnesses and five model backends.

AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents

Evaluated models: GPT-5.5, Gemini 3.1 Pro, DeepSeek V4-Pro +2 more

Source: arXiv

Malicious GitHub Issue Injection Against Coding Agents

Published 7/22/2026

Analyzed 8/13/2026

IssueTrojanBench studies indirect prompt injection when a coding agent processes an apparently ordinary software-development issue or related artifact. Starting with six legitimate seed issues from two Python repositories, the authors construct 696 adversarial issue variants spanning four unsafe-action families and six delivery formats, then execute those variants across six agent-model configurations.

IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests

Evaluated models: GPT-5.3 Codex, GPT-5.4, Claude Sonnet 4.6

Source: arXiv

Third-Party Agent Skill Supply-Chain Execution Risks

Published 7/22/2026

Analyzed 8/13/2026

OpenSkillRisk evaluates whether agent harnesses safely handle third-party skills that introduce risky behavior through otherwise plausible, benign tasks. The benchmark assembles 263 risky skills from public agent-skill ecosystems and tests three CLI-agent harnesses against seven risk categories using isolated task workspaces, mocked external services, and execution-level evidence.

OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills

Evaluated models: GPT-5.1 Codex Mini, GPT-5.3 Codex, GPT-5.4 +10 more

Source: arXiv

Prompt injection across agent privilege boundaries

Published 7/21/2026

Analyzed 9/9/2026

Untrusted issue descriptions and tool responses can redirect privileged coding and tool agents. Twin Agent separates exploration from execution and restricts the information exchanged between them.

Twin Agent: Context Residual Compression for Privilege Separated Agents

Evaluated models: GPT-5.2, Gemini 2.5 Flash, Gemini 3.5 Flash

Source: arXiv

Unsafe shell commands can pass LLM agent dispatch guards

Published 7/21/2026

Analyzed 9/9/2026

Shell-command screening can miss harmful actions generated by coding agents. CARE combines static checks with optional model review at the command-dispatch boundary.

CARE: Pre-Execution Command Verification for Shell-Executing LLM Agents

Evaluated models: Qwen3-Coder-30B-A3B-Instruct, Granite Guardian 2B, Llama Guard 3 8B +1 more

Source: arXiv

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