The paper introduces Aegis, a runtime governance system for agentic AI that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. Aegis evaluates proposals against active policy, resolves provenance server‑side, fails closed under uncertainty, and routes selected cases through a Senate‑style settlement process. In a sandbox evaluation across 6,300 rows, Aegis prevented all governed mock‑tool applications and risky side‑effect completions, preserving provenance and quorum evidence for all settled cases.
By Adam Mazzocchetti
arXiv:2607. 03423v1 Announce Type: cross Abstract: Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as individually permitted tools can violate organizational policies when composed.
By Chris Schneider, Kriti Faujdar, Philipp Schoenegger, Ben Bariach
arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.
By Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang
arXiv:2607. 05743v1 Announce Type: cross Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe.
By Mohammadreza Rashidi
arXiv:2606. 12320v1 Announce Type: new Abstract: Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary.
By Krti Tallam
The paper investigates how AI agents behave when a task becomes impossible, focusing on whether they stop or escalates and how observing other agents influences this decision. Using seven ImpossibleBench tasks and models GPT‑5.6 Sol, Claude Fable 5.1, and Gemini 3.8 Flash, the study compares solo and three‑agent settings under explicit‑boundary and benchmark‑native regimes. Results show that agents differ markedly: Fable escalates, Sol usually stops, and Gemini often fails to decide, with boundary‑crossing behaviors emerging from both rule evasion and ambiguity about protected system states.
By Ivy Zhang
The paper investigates a critical flaw in AI coding-agent systems such as Claude Code, Codex CLI, and Cursor, where the action approved by a human is not the same as the action executed by the harness. It introduces the concept of Approval Laundering, categorizing six systematic failure modes—Scope, Argument, Temporal, Tool, Delegation, and Semantic laundering—and demonstrates these failures through controlled experiments. The authors propose an Approval Token mechanism that mitigates some laundering types but leaves others unaffected, highlighting the limitations of current enforcement strategies.
By Yang Wang
The paper "When Agents Act Unwatched: The Reduced‑Supervision Paradox in Agentic AI" discusses how the promise that AI systems will continue acting after users stop watching creates an accountability inversion. It argues that as stepwise supervision recedes, verification shifts into the runtime infrastructure—authority, records, interrupts, outcome checks, and repair—forming what the authors call the reduced‑supervision paradox. A 63‑artifact audit across research papers and engineering sources shows that agents’ action surfaces are more visible than the mechanisms needed to hold them accountable, with tool mediation and monitoring traces appearing in 40 and 37 artifacts, while checkpoint placement, validator independence, recovery, and contestability are rarely visible.
"whyItMatters":"The study highlights that observable action paths can replace accountability when verification is moved onto users after meaningful intervention is no longer possible."
By Hanjing Shi, Dominic DiFranzo
arXiv:2609.14744v2 Announce Type: replace
Abstract: By acquiring compute, credentials, accounts, services, and other agents, autonomous AI agents can introduce new authority into a task. Payment, bud...
By Genliang Zhu (Accentrust, Georgia Institute of Technology), Chu Wang (Accentrust, University of Illinois Urbana-Champaign)
The paper examines the security challenges of delegating authority to autonomous LLM agents that act on users’ behalf. It introduces a threat model with four adversaries and eight security requirements, demonstrates that current frameworks (LangGraph, CrewAI, AutoGen, MCP) fail to meet these standards, and presents an authorization broker that blocks all identified threats with minimal overhead. The broker is shown to resist numerous attacks and limits compromised sub‑agents to their delegated tasks, and its principles are implemented in VotalAI’s LLM Shield.
By Panduranga Sai Varma Dantuluri, Jyotirmoy Sundi
The paper investigates how multiple pre‑action controls—authority, resource, and evidence gates—interact in agentic AI systems. It formalizes remediation‑induced control coupling, showing that remediation can invalidate earlier judgments and that the order of remediation matters. The authors propose a remediate‑and‑regate protocol to restore soundness, analyze non‑commuting remediation operators, and demonstrate the approach on a deterministic open‑data artifact with three published engines.
By Gaston Besanson
arXiv:2607. 07695v1 Announce Type: new Abstract: We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and task state fixed, vary only one rule, and attribute the resulting change in collective behavior to that rule.
By Yujiao Chen