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arxiv: 2606.05567 · v1 · pith:H5WRWURMnew · submitted 2026-06-04 · 💻 cs.CR · cs.MA

ZERO-APT: A Closed-Loop Adversarial Framework for LLM-Driven Automated Penetration Testing under Intelligent Defense

Anlan Zheng , Tiantian Zhu This is my paper

Pith reviewed 2026-06-28 01:24 UTC · model grok-4.3

classification 💻 cs.CR cs.MA
keywords penetration testingLLM agentsadversarial frameworkcausal consistencyauditabilityintelligent defenseclosed-loop systempost-exploitation
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The pith

ZERO-APT places an LLM attacker in a closed loop against a live LLM defender that processes real telemetry, while architectural rules enforce attack-chain consistency and a judge produces traceable reports.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

LLM-driven penetration testing agents have so far been measured only against static targets that never detect or block attacks, leaving their behavior against responsive defenses unknown. At the same time, the causal links in their multi-step attack sequences rest on fragile LLM reasoning, and the reasons behind each choice stay hidden from analysts. ZERO-APT folds realism, consistency, and auditability into one turn-based attacker-defender-judge structure: the defender consumes Sysmon data and reacts in real time, three fixed mechanisms separate planning from execution and filter invalid actions, and the judge keeps global state while emitting structured CTI reports. On a Windows Server 2022 post-exploitation prototype run across five scenarios and three defender settings, the system records higher attack success and stronger consistency than earlier agents while making every decision reviewable. The released benchmark lets future work measure agents under the same live-opponent conditions.

Core claim

ZERO-APT is a turn-based attacker-defender-judge framework that embeds a configurable LLM defender consuming Sysmon telemetry to create a live opponent, shifts causal consistency from LLM reasoning into three architectural mechanisms (planning-execution separation, multi-dimensional ReAct feedback, and hard-constraint-filtered action library), and uses a dedicated judge to maintain state and emit structured CTI reports that render every decision traceable. In evaluations on a Windows Server 2022 post-exploitation setup the framework records 79% attack success rate and 0.860 causal consistency score together with end-to-end auditability.

What carries the argument

The turn-based attacker-defender-judge architecture whose three mechanisms move causal consistency from unstable LLM reasoning into enforced system design: planning-execution separation, multi-dimensional ReAct feedback, and hard-constraint-filtered action library.

If this is right

  • Penetration testing agents can be evaluated against responsive, intelligent defenses instead of passive targets.
  • Causal consistency of attack chains becomes a property of the system architecture rather than prompt quality alone.
  • Every agent decision is recorded in structured CTI reports that a human analyst can review after the fact.
  • The released benchmark supplies a standardized way to compare future agents under adversarial defense conditions.
  • Defender configuration options allow controlled testing against different strengths of intelligent response.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same closed-loop structure could be applied to other sequential decision tasks where one LLM agent must operate against another that can detect and block its moves.
  • Auditability through judge-generated reports may make LLM agents more acceptable in settings that require human oversight or regulatory review.
  • If the mechanisms hold across domains, they could reduce reliance on prompt engineering for reliable long-horizon behavior in adversarial environments.
  • Adding a human review step at the judge layer would test whether the current traceability already meets practical operational needs.

Load-bearing premise

The three mechanisms of planning-execution separation, multi-dimensional ReAct feedback, and hard-constraint action filtering are together sufficient to enforce causal consistency even when the underlying LLM reasoning remains unreliable.

What would settle it

An experiment in which an attacker built with the three mechanisms still produces frequent causal breaks (such as attempting an action that contradicts an earlier confirmed step) while facing an active defender.

Figures

Figures reproduced from arXiv: 2606.05567 by Anlan Zheng, Tiantian Zhu.

Figure 1
Figure 1. Figure 1: ZERO-APT overall architecture. Attacker executes actions and receives Judge feedback; Defender detects from Sysmon logs via ELK Stack; [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Two backtracking mechanisms. Red: passive backtracking, cross-round Judge-driven. Blue: active backtracking, same-round Dispatcher-driven. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Attack success rate by Defender intensity. (a) ASR aggregated by [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Defender detection performance across three intensities (ZERO [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: CCS-ASR scatter plot with CBF% annotations. Distribution of [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original abstract

LLM-driven automated penetration testing agents are typically evaluated against static targets that neither detect nor respond to attacks, so their behavior under intelligent defense remains untested. The causal consistency of multi-step attack chains likewise hinges on unstable LLM reasoning, and agent decisions remain opaque to human analysts. These three shortcomings, in realism, consistency, and auditability, are usually patched in isolation. We present ZERO-APT, a turn-based attacker-defender-judge framework that addresses them within a single architecture. For realism, ZERO-APT embeds a configurable LLM Defender that consumes Sysmon telemetry and detects attacks in real time, exposing the attacker to a live opponent rather than a passive target. For consistency, three architectural mechanisms move causal consistency from unstable LLM reasoning into enforced system architecture: separation of planning from execution, multi-dimensional ReAct feedback, and a hard-constraint-filtered action library. For auditability, a dedicated Judge agent adjudicates each round, maintains global state, and emits structured post-hoc CTI reports that make every decision traceable. We evaluate a Windows Server 2022 post-exploitation prototype across five scenarios with three Defender configurations. ZERO-APT reaches 79\% attack success rate (Aurora 22\%, PentestGPT 39\%), a Causal Consistency Score of 0.860 (Aurora 0.930, Claude Code 0.520), and end-to-end decision auditability through structured CTI reports. We release the benchmark to support evaluation of penetration agents under intelligent defense.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper presents ZERO-APT, a closed-loop turn-based attacker-defender-judge framework for evaluating LLM-driven automated penetration testing agents against intelligent defenses. It claims to address realism via an LLM Defender consuming Sysmon telemetry, causal consistency via three architectural mechanisms (planning-execution separation, multi-dimensional ReAct feedback, hard-constraint-filtered action library), and auditability via a Judge agent producing structured CTI reports. On a Windows Server 2022 post-exploitation prototype across five scenarios, it reports 79% attack success rate (Aurora 22%, PentestGPT 39%), Causal Consistency Score 0.860 (Aurora 0.930, Claude Code 0.520), and end-to-end decision traceability, while releasing the benchmark.

Significance. If the empirical results hold under scrutiny, the work supplies a needed benchmark for testing penetration agents against responsive defenses rather than static targets, and the benchmark release supports reproducibility. The integration of defender and judge components into a single architecture could inform designs for more robust LLM security agents, provided the claimed architectural contributions to consistency are isolated.

major comments (1)
  1. [Evaluation/Results] Evaluation/Results section: The central claim attributes the Causal Consistency Score of 0.860 to the three architectural mechanisms enforcing consistency at the system level. However, only aggregate scores for the full system are reported across the five scenarios; no ablation studies, component-wise measurements, or comparisons to variants with individual mechanisms disabled are provided. This leaves open whether the score derives from the base LLM, prompt engineering, or scenario selection rather than the claimed architectural enforcement.
minor comments (1)
  1. [Abstract] Abstract and methods: Details on computation of the Causal Consistency Score, selection criteria for the five scenarios, exact baseline implementations, and any error bars or statistical tests are not summarized, complicating verification of the reported performance deltas.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment point by point below and commit to revisions that directly respond to the concern raised.

read point-by-point responses

  1. Referee: [Evaluation/Results] Evaluation/Results section: The central claim attributes the Causal Consistency Score of 0.860 to the three architectural mechanisms enforcing consistency at the system level. However, only aggregate scores for the full system are reported across the five scenarios; no ablation studies, component-wise measurements, or comparisons to variants with individual mechanisms disabled are provided. This leaves open whether the score derives from the base LLM, prompt engineering, or scenario selection rather than the claimed architectural enforcement.

    Authors: We acknowledge that the Evaluation section reports only aggregate results for the complete ZERO-APT system and does not include ablation studies that disable the individual mechanisms (planning-execution separation, multi-dimensional ReAct feedback, and hard-constraint-filtered action library). The comparisons to Aurora and PentestGPT establish overall performance differences but do not isolate the contribution of each architectural component within our framework. We agree that this leaves the attribution of the 0.860 score open to alternative explanations such as base LLM capabilities or prompt design. In the revised manuscript we will add ablation experiments that systematically disable each mechanism in turn and report the resulting Causal Consistency Scores across the five scenarios, thereby providing direct empirical evidence for the claimed system-level enforcement. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results independent of internal definitions

full rationale

The paper describes a system architecture and reports aggregate empirical metrics (79% ASR, 0.860 CCS) against external baselines (Aurora, PentestGPT) across five scenarios. No equations, fitted parameters, or derivations are present that reduce any claimed result to its own inputs by construction. The three mechanisms are presented as design choices whose contribution is asserted but not isolated via ablations; however, this is a verification gap rather than circularity. No self-citations are invoked as load-bearing uniqueness theorems, and the Causal Consistency Score is not defined in terms of the mechanisms themselves. The evaluation is self-contained against external comparators.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework depends on the domain assumption that architectural constraints can override LLM reasoning instability, and introduces the judge as a new entity without external validation beyond reported metrics.

axioms (1)
  • domain assumption The LLM defender can effectively detect attacks using Sysmon telemetry in real time
    This is invoked in the design of the defender component to provide realistic opposition.
invented entities (1)
  • Judge agent no independent evidence
    purpose: Adjudicates each round, maintains global state, and emits structured post-hoc CTI reports
    New component introduced to address auditability shortcoming.

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