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arXiv cs.AI· Simon Hegelich, Bardh Prenkaj, Habiba Sarhan, Morteza Shahrezaye, Kolja Hegelich, Christian Montag, Ralf Schneider, Erik Cambria·· 3 小时前AI 评分38

ActiveSecHarness:面向智能体编程框架的组合式安全防御架构

From Attack Surface to Defense Architecture: Compositional Security for Agentic Coding Harnesses

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一篇 arXiv 论文提出 ActiveSecHarness,一种基于“智能与权限分离”的神经符号参考监控架构,用于防御 AI 编程 harness 中被注入指令后利用 bash、curl 等双用途二进制的攻击。

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Abstract:AI coding harnesses couple language models to shells, filesystems, and network sockets, turning the harness into an automated Living-off-the-Land (LotL) platform: once an adversary injects instructions into retrieved content, dual-use binaries such as bash, curl, and find become the payload. We argue that security in agentic systems is a structural property of the cognitive architecture and permission composition rather than an emergent property of individual models. Static regex blacklists miss structural evasions, whereas LLM-as-a-Judge guardrails incur high latency while over-blocking benign work.
We present ActiveSecHarness, a neurosymbolic reference-monitor architecture built around Separation of Intelligence and Authority. It combines (1) Macaroon-inspired HMAC capability tokens across four Trust and Authority Rings, (2) a 2D product taint lattice $\mathcal{L} = \mathcal{L}_{\mathrm{trust}} \times \mathcal{L}_{\mathrm{sens}}$ and a workspace provenance ledger that prevents secret laundering across disk, (3) deterministic Ring-0 OS/tool guards, and (4) bytecode certificates, split-horizon audit logs, self-healing security test synthesis, and an adaptive UserWikiManager.
Across $N=252$ fixtures ($120$ attack, $120$ benign, $12$ kill chains), a live local LLM judge (qwen3:4b) refuses $98.3\%$ of benign work at $142.3\,\mathrm{ms}$ latency, whereas Ring 0 achieves $\mathrm{ABR}=1.000$ and $\mathrm{FPR}=0.000$ in $12.7\,\mu\mathrm{s}$ ($11{,}200\times$ faster). In a multi-step trajectory ablation, an oracle Conseca-style stateless policy misses $4/12$ kill chains ($\mathrm{ABR}=0.667$) and coarse Boolean taint blocks $83.3\%$ of benign workflows, while our 2D product lattice achieves $\mathrm{ABR}=1.000$ and $\mathrm{FPR}=0.000$ at $\approx 20\,\mu\mathrm{s}$ per step.
Comments: 21 pages, 3 figuers, 1 sublement
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
MSC classes: 68M25, 68T42, 06B35, 68Q60
ACM classes: D.4.6; K.6.5; I.2.11; D.2.4
Cite as: arXiv:2611.13960 [cs.CR]
  (or arXiv:2611.13960v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2611.13960

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Simon Hegelich [view email]
[v1] Tue, 10 Nov 2026 11:52:40 UTC (130 KB)

来源:arXiv cs.AI · arxiv.org