An Information Flow Graph monitor is introduced that analyzes structural security regressions using control-flow and data-flow graph diffs alongside raw code diffs and suggests that untrained structural monitors provide a practical and auditable path toward democratizing deployment safety for organizations seeking trustworthy adoption of advanced AI agents.
Abstract
AI software development agents are increasingly capable of modifying infrastructure and security critical systems, creating risks where an agent completes its assigned task while covertly weakening safeguards through actions such as broadening permissions, degrading logging, or introducing persistence mechanisms. While frontier laboratories may deploy sophisticated monitoring pipelines, many organizations and individual users adopting coding agents lack the resources and governance maturity required to maintain complex learned monitor ensembles. We study this problem as infrastructure level sabotage under task success in the infrastructure-as-code setting from ControlArena and compare trained and untrained monitors across synchronous and asynchronous deployment modes. We introduce an Information Flow Graph (IFG) monitor that analyzes structural security regressions using control-flow and data-flow graph diffs alongside raw code diffs. In asynchronous evaluation, an untrained git diff monitor misses 11.6% attacks at 1% false positive rate auditing budget, our untrained IFG monitor reduces this to 3.5%, and the trained Async RF monitor achieves below 1%. Unlike the trained Async RF monitor, IFG can also operate synchronously as a pre-deployment safeguard that blocks suspicious infrastructure changes before execution or merge. In synchronous mode, IFG realtime rollback reduces joint main task and covert task success from 74.4% to 0.0% with no measurable reduction in legitimate task completion. These results suggest that untrained structural monitors provide a practical and auditable path toward democratizing deployment safety for organizations seeking trustworthy adoption of advanced AI agents.
As artificial intelligence systems transition from task-specific tools to autonomous agents capable of complex decision-making, traditional external oversight mechanisms become inadequate for ensuring safety, security, and alignment. This paper introduces a governance and security-by-design framework that embeds responsibility mechanisms directly into agentic AI architectures, enabling continuous self-monitoring and alignment verification through multi-agent governance systems. We demonstrate that external governance approaches fail to scale with system autonomy, creating temporal gaps between assessment and deployment that enable ungovernable behaviors. Through mathematical modeling using stochastic differential equations, we formalize how competing objectives in agentic systems create systematic interference patterns that degrade safety properties. Our empirical validation across 800 experiments reveals three critical failure modes:
(1) systematic security vulnerabilities from AI-generated code, with efficiency-focused prompting introducing memory safety issues in 42.7% of cases, while security-focused prompting paradoxically creates cryptographic vulnerabilities in 21.1% of cases;
(2) iterative degradation, where security vulnerabilities increase by 37.6% after just five feedback iterations; and
(3) knowledge dilution, where domain expertise degrades by 47% as irrelevant context accumulates.
Our multi-agent governance and security architecture, validated through industry partnerships, achieves a 40% reduction in post-deployment safety incidents while maintaining operational capability. These findings establish that safe, secure, and aligned agentic systems require architectural integration of governance mechanisms that address the dynamic, multi-objective nature of autonomous AI systems.
Unknown authors· Proceedings of IASEAI Confer...· 0 citations
Managing modern computing infrastructure has become a steadily harder problem due to the ever-increasing complexity. Recent advances in AI agents create a timely opportunity to automate infrastructure management tasks, but it remains unclear how well such agents can handle real-world infrastructure complexity. We present InfraBench, a benchmark suite for evaluating AI agents on realistic infrastructure tasks across the full system stack and full operational lifecycle with fine-grained risk assessment. Experiments with 15 agent-model configurations show that even the strongest agent cannot secure a full score across all tasks. Mean effective scores range from roughly 40% to 88% (with per-configuration standard errors of 6-12 points), repeating every task three times reveals that top configurations still pass only a fraction of their attempts, and per-check scoring exposes a general failure pattern: agents may routinely satisfy short-term objectives while leaving non-durable changes, broken distributed invariants, unsafe side effects, and uncleaned state behind. INFRABENCH, including its live leaderboard, tasks, and evaluation harness, is publicly available at infraben.ch.
Yuan Gao, Zeren Yang, Junnan Li et al.· 0 citations
RedAgentBench is introduced, an executable framework for autonomous red-teaming and faithful measurement that shows that executable evaluation can improve safety measurement and identify actionable intervention points.
Zixing Chen, Xingyuan Liu, Jie Zhu et al.· 1 citation
This paper presents a large-scale empirical study using the AIDev dataset to systematically characterize security code smells in agent-generated pull requests (PRs), finding that human collaborators are responsible for introducing 67.6% of genuine leaked secrets within these agent-assisted workflows.
A. H. M. Nazmus Sakib, Dipayan Banik, Murtuza Jadliwala· arXiv.org· 0 citations
A four-dimensional Integration Friction Index is introduced that separates one-time engineering cost from recurring organisational, legal, and maintenance cost and shows why scope and budget enforcement cannot be delegated to system prompts.
Israt Moyeen Noumi, Tarannum Ahmed Nowshin, Md. Mehedi Hasan Bhuiyan Nipu et al.· 0 citations
This work presents AHA, a falsifiable discovery loop that proposes a vulnerability hypothesis, constructs a falsifier, instantiates a valid attack, executes it in a sandboxed harness, reflects on the trajectory, and promotes confirmed findings into a Vulnerability Concept Graph (VCG), which provides an auditable artifact for production safety teams to inspect vulnerabilities, validate patches, and accumulate reusable safety knowledge.
