Attack Context

Coding agents Research

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

14 matching entries out of 16 in this category

Coding agents research results

14 entries

Context privilege escalation in AI agent harnesses

Published Sep 1, 2026Analyzed Sep 9, 2026Source: arXiv

Context assembly can promote repository, tool or skill content into higher-priority instructions or persistent state. The paper studies 12 pinned agent-harness versions.

What's in Your Agent's Context? Context Privilege Escalation Attacks against AI Agent Harness

Evaluated models: GPT-5.5, GPT-5.4-mini, Claude Sonnet 4.6+4 more

GPT-5.5
GPT-5.4-mini
Claude Sonnet 4.6
Claude Opus 4.6
Gemini 2.5 Flash
Gemini 2.5 Pro
DeepSeek V4 Flash

Poisoned agent skills amplify coding-agent token use

Published Aug 22, 2026Analyzed Sep 9, 2026Source: arXiv

Third-party skills can inflate coding-agent resource use while an otherwise legitimate task remains functional.

SkillBloat: Token Amplification Attacks via Skill Injection in LLM Coding Agents

Evaluated models: GLM-4.7-Flash, GLM-5, GPT-5.4-mini+1 more

GLM-4.7-Flash
GLM-5
GPT-5.4-mini
GPT-5.5

Trajectory Poisoning in Self-Evolving Agent Skills

Published Aug 6, 2026Analyzed Aug 13, 2026Source: arXiv

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

GPT-5.4
MiniMax M2.5
DeepSeek V3.2
DeepSeek V4-Pro
Qwen3.5-35B-A3B
Qwen3.5-122B-A10B

Dynamic Security Testing for Third-Party Agent Skills

Published Aug 4, 2026Analyzed Aug 13, 2026Source: arXiv

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

Persistent Agent Memory Poisoning and Incomplete Repair

Published Jul 29, 2026Analyzed Aug 13, 2026Source: arXiv

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

Workspace Agent Runtime Safety Failures Across Risk Carriers

Published Jul 29, 2026Analyzed Aug 13, 2026Source: arXiv

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

GPT-5.5
Gemini 3.1 Pro
DeepSeek V4-Pro
MiniMax-M3
Qwen3.7-Plus

Malicious GitHub Issue Injection Against Coding Agents

Published Jul 22, 2026Analyzed Aug 13, 2026Source: arXiv

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

Third-Party Agent Skill Supply-Chain Execution Risks

Published Jul 22, 2026Analyzed Aug 13, 2026Source: arXiv

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

GPT-5.1 Codex Mini
GPT-5.3 Codex
GPT-5.4
Gemini 2.5 Pro
Gemini 3 Flash
Gemini 3.1 Pro
Claude Haiku 4.5
Claude Sonnet 4.6
Claude Opus 4.6
DeepSeek V3.2
DeepSeek V4-Pro
Kimi-K2.6
GLM-5.1

Prompt injection across agent privilege boundaries

Published Jul 21, 2026Analyzed Sep 9, 2026Source: arXiv

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

Unsafe shell commands can pass LLM agent dispatch guards

Published Jul 21, 2026Analyzed Sep 9, 2026Source: arXiv

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

Qwen3-Coder-30B-A3B-Instruct
Granite Guardian 2B
Llama Guard 3 8B
GPT-oss Safeguard 20B

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