AutoDefense: Autonomous Cyber Defense

Updated 22 February 2026

AutoDefense is a comprehensive approach featuring autonomous frameworks that employ AI, reinforcement learning, and game-theoretic methods to detect, plan, and respond to cyber threats.
Systems using AutoDefense integrate perception modules, decision engines, and actuator layers to execute real-time preventive, self-healing, and moving target actions across diverse infrastructures.
Empirical studies show AutoDefense architectures significantly reduce attack success rates and balance false positives with operational overhead in various cyber environments.

AutoDefense refers to a set of architectures, algorithms, and frameworks supporting fully or partially autonomous cyber defense—by which defensive operations are carried out by software agents rather than exclusively by human operators. AutoDefense encompasses agent- and multi-agent systems for cyber, AI/ML, and LLM workloads, spanning classic blue-team cyber defense, adversarial ML defense, and advanced moving-target and self-healing strategies. The concept is defined by architectures with closed-loop detection, planning, response, and learning cycles, supporting real-time response to adversarial activity, resilience against novel and adaptively evolving threats, and (in some settings) explainable or risk-aware operation. This article synthesizes foundational architectures, core algorithmic principles, system instantiations, and empirical evidence for AutoDefense as reported in recent literature.

1. Core Concepts and Architectural Paradigms

AutoDefense leverages AI, RL, and game-theoretic approaches to create agents—software entities capable of sensing, analyzing, responding to, and learning from cyber threats with minimal human intervention. These systems operate across networked environments, cloud infrastructures, ML model endpoints, and even LLM inference interfaces.

The foundational architecture includes:

Perception (Sensing) Module: Ingests raw network flows, host logs, sensor data, or application-level artifacts. Pre-processing and feature extraction produce structured state representations.
Decision and Planning Engine: Implements threat detection (via ML classifiers, anomaly detection, or hand-coded logic) and defensive policy, often framed as an (PO)MDP, SMDP, or sequential game. Decision-making is further informed by risk assessment and constraint checking, enforcing safe and mission-constrained behaviors.
Actuator/Enforcement Layer: Executes remediation actions—ranging from traffic blocking, host isolation, honeypot deployment, to AI model hardening or LLM response filtering.
Learning and Adaptation Loop: Updates policies via reinforcement learning, adversarial training, or supervised retraining; may include continual (online) learning to maintain efficacy against evolving threats.
Human Interaction and Feedback: @@@@1@@@@ for action validation, policy tuning, or labeling edge cases, combined with explainability (XAI) mechanisms such as SHAP or post-hoc attribution.

This paradigm generalizes across domains, from cyber network defense (Oesch et al., 2024, Vyas et al., 2023, Ligo et al., 2022, Lucia et al., 2019), adversarial ML and robust perception (Kalin et al., 2021, Barletta et al., 23 Jul 2025, Peng et al., 9 Feb 2026), to LLM jailbreaking resistance (Zeng et al., 2024, 2506.23576, Lu et al., 2024, &&&10&&&).

2. Formal Models and Algorithmic Approaches

AutoDefense agents are frequently formalized as (partially observed) Markov decision processes (MDPs/POMDPs):

$\mathcal{M} = (\mathcal{S}, \mathcal{A}, P, R, \gamma)$

$\mathcal{S}$ : state space (network configuration, adversary status, etc.)
$\mathcal{A}$ : action space (defensive actions)
$P$ : stochastic transition kernel
$R$ : reward function, often encoding tradeoffs across confidentiality, integrity, and availability (CIA triad), sometimes augmented with risk penalties or resilience metrics.
$\gamma$ : discount factor.

Agents optimize a policy $\pi_\theta(a|s)$ for expected discounted return:

$J(\theta) = \mathbb{E}_{\pi_\theta}\left[ \sum_{t=0}^\infty \gamma^t R(s_t, a_t, s_{t+1}) \right]$

Multi-agent settings decompose the task into specialized subagents for detection, response, containment, and recovery, with hierarchical or peer coordination as in real-world SOC workflows (Oesch et al., 2024, Foley et al., 2023, Foley et al., 2024).

Learning algorithms include:

Deep RL (DQN, PPO, A2C, Actor–Critic frameworks) (Zeng et al., 2024, Foley et al., 2023, Foley et al., 2024, Vyas et al., 2023, Molina-Markham et al., 2021)
Hierarchical and multi-agent RL (manager/worker or coordinated agent teams)
Game-theoretic strategic learning for defensive deception and honeypot engagement (Huang et al., 2019)
Adversarial training and ensemble learning for robust ML/AI defenses (Kalin et al., 2021, Barletta et al., 23 Jul 2025)
Evolutionary prompt optimization and auto-healing loops for LLM defense (Sivaroopan et al., 27 Jan 2026)

3. Specialized Instantiations: Networks, ML, and AI Workloads

Network and Infrastructure Defense

AutoDefense agents for networked systems ingest detector or SIEM data, construct state representations including topology and history, and invoke RL-trained policies for defense actions (Oesch et al., 2024). Evaluation frequently leverages simulated environments (e.g., CybORG, FARLAND) with varied attacker models and topologies (Oesch et al., 2024, Foley et al., 2023, Foley et al., 2024, Molina-Markham et al., 2021).

Reward structures penalize compromise, disruption, and costly actions: $r_t = -0.1 H_{\mathrm{admin}}(t) - 1 S_{\mathrm{admin}}(t) - 10 \mathbb{I}_\text{disrupt}(t) - 1 \mathbb{I}_\text{restore}(t)$ (Foley et al., 2024).

Multi-agent frameworks assign agents to each NIST lifecycle phase (Identify, Protect, Detect, Respond, Recover), supporting modular design, explainability, and rapid adaptation (Oesch et al., 2024). Human feedback closes the loop via action approval and shaping rewards, while auditability is ensured via logging and XAI (Oesch et al., 2024, Vyas et al., 2023).

AI/ML and LLM Defense

For adversarial ML, AutoDefense orchestrates red-team/blue-team/green-team automation to adversarially probe, ensemble-harden, and iteratively retrain classifiers—e.g. in overhead imagery with multi-modal data (Kalin et al., 2021), or by controlling window-of-opportunity in Random Forest/GB ensembles for in-vehicle IDS (Barletta et al., 23 Jul 2025).

LLM-centric AutoDefense frameworks (notably (Zeng et al., 2024, 2506.23576, Lu et al., 2024, Sivaroopan et al., 27 Jan 2026)) employ multi-agent LLM filtering pipelines. Systems such as "AutoDefense" (Zeng et al., 2024, 2506.23576) and "SHIELD" (Sivaroopan et al., 27 Jan 2026) advance defensive architectures through:

Response filtering by collaborative agent pipelines (IntentionAnalyzer, PromptAnalyzer, Judge) post-victim-LLM generation.
Ensemble-of-defenders (MoD) routing based on prompt classification and DAG-modeled dependencies (Lu et al., 2024).
Self-healing: upon detection failure, knowledgebase update and defense prompt optimization are autonomously looped in (Sivaroopan et al., 27 Jan 2026).

These designs achieve low attack success rates for jailbreaks, preserve faithful response alignment, and allow extensionality for new attack classes with minimal retraining overhead.

Moving Target and Infrastructure Defense

AutoDefense in cloud-native/AI-infrastructure settings (e.g., ADA (Sheriff et al., 27 May 2025)) exploits proactive moving target defense via continual rotation (churn) of pods, mathematically bounding attacker dwell-time and kill-chain success:

$P_{\mathrm{success}} = e^{-\lambda t_a}$

where $\lambda$ is churn rate, $t_a$ is attacker exploit time. System performance guarantees and tradeoffs are quantified by overhead and attacker survival probability.

4. Requirements, Resilience, and Explainability

Modern AutoDefense agents are evaluated against stringent requirements: generalization to unseen topologies, continual learning, explainability (XAI), adversarial resilience (poisoning/evasion), multi-agent coordination, and real-time or near-real-time operation (Vyas et al., 2023, Théron et al., 2019, Lucia et al., 2019).

Explainability is enforced via post-hoc ablation, SHAP analysis, integrated gradients, and in LLM cases, agent rationales. Resilience is enforced by adversarial training, formal constraint checking, and explicit risk metrics in learning objectives (Ligo et al., 2022, Vyas et al., 2023). Feedback loops include both online autonomy and slower human-in-training/validation cycles.

Risk of negative externalities (functional, safety, ethical) is mitigated by constraint checkers, Asimov’s-law inspired rule filters, formal verification, and human-in-the-loop override (Ligo et al., 2022). Auditability—immutable logs and compliance metrics—is central for trust-building.

5. Empirical Results and Performance Evaluation

AutoDefense frameworks consistently demonstrate substantial reductions in successful attacks and tradeoffs with false-positive rates and system overhead:

LLM AutoDefense: Attack Success Rate (ASR) reductions from ~55% (no defense) to ~8% (3-agent) with LLaMA-2-13B; FPRs ~6–7% (Zeng et al., 2024, 2506.23576).
Detector-based RL agents outperform raw-log baselines on network defense (SR: 84% vs. 72%; containment time: 14.7 vs. 18.2 steps) (Oesch et al., 2024).
Multi-modal ensemble defenses in ML cut adversarial robustness losses from ~50% (mono-channel) to ~7% (IR-only) or <10% (optimized ensemble) (Kalin et al., 2021).
Proactive pod rotation in ADA halves attacker success probability per doubling of churn rate, with modest resource costs (Sheriff et al., 27 May 2025).
Full-cycle, agentic defenses achieve persistent F1 scores >99% against LLM sponge/resource exhaustion attacks with adaptation (Sivaroopan et al., 27 Jan 2026).

Performance evaluation increasingly blends simulated environments, red-blue adversarial self-play, and real-world or cloud-native deployment, always reporting standard detection (TP, FP, FN, F1, ROC) and resilience metrics (MTTD, P_success, compliance rates).

6. Limitations, Trade-Offs, and Future Directions

Primary limitations include:

Generalizability: Many AutoDefense agents underperform on adversary tactics never encountered in training; robust continual/meta-learning remains underdeveloped (Foley et al., 2023, Molina-Markham et al., 2021).
False Positives & Overhead: Increasing detection agents reduces false negatives but may spike false positives and latency, especially in LLM defense (2506.23576).
Adversarial Resilience: Algorithmic robustness to poisoning and evasion is an unresolved challenge; gym environments often lack adequate support for adversarial training (Vyas et al., 2023).
Explainability: Most explainability is post-hoc; intrinsic, real-time XAI remains an open frontier.
Scaling & Practicality: Many frameworks’ evaluation is confined to small-scale emulated or simulated networks; scaling, dynamic topology, and integration with operational infrastructure are ongoing research targets.

Emerging research directions:

Dynamic/adaptive agent assignment, content-aware workflow selection, and meta-agent architectures in LLM filtering (Zeng et al., 2024).
Automatic expert weighting and defense fusion for ensemble-based LLM defense (Lu et al., 2024).
Full end-to-end automation in adversarial ML defense pipelines, including data ingestion, architecture search, and retraining (Kalin et al., 2021, Barletta et al., 23 Jul 2025).
Risk-adaptive infrastructure MTD, confounding attacker timing and integrating confidential computing (Sheriff et al., 27 May 2025).
Cognitive/hybrid agent architectures for richer adversarial reasoning in contested C4ISR and military environments (Théron et al., 2019).
Formal benchmarking on distributional robustness, adversarial resilience, explainability, and operational trustworthiness remains central (Vyas et al., 2023, Molina-Markham et al., 2021).

7. Broader Impact, Ethical, and Societal Considerations

Ethical, legal, and societal implications of AutoDefense technology include the risks of autonomous negative externalities (e.g., mission harm, privacy violation, escalatory responses), challenges of certifying self-modifying software, trust in agentic autonomy, and global arms control (Théron et al., 2019, Ligo et al., 2022).

Trusted deployment demands transparent, auditable, human-overridable frameworks, alongside industry and policy standards for agent architecture, verifiability, and ethical operation.

AutoDefense, as a field, offers both a blueprint for practical autonomous defense deployments and a research agenda confronting the evolving threat landscape of networked, ML-powered environments. Its unifying themes—multi-agent autonomy, dynamic learning and adaptation, explainability, adversarial resilience, and operational trust—are carrying over into new domains, from battlefield IoBT nodes and smart city vehicles to critical cloud-native AI services and universal LLMs. The field is characterized by rapid progress, cross-disciplinary synthesis, quantitative evaluation, and evolving standards of trustworthiness and human oversight.

Key references: (Oesch et al., 2024, Vyas et al., 2023, Zeng et al., 2024, 2506.23576, Sivaroopan et al., 27 Jan 2026, Sheriff et al., 27 May 2025, Molina-Markham et al., 2021, Théron et al., 2019, Foley et al., 2023, Foley et al., 2024, Kalin et al., 2021, Ligo et al., 2022, Barletta et al., 23 Jul 2025, Peng et al., 9 Feb 2026, Lucia et al., 2019, Huang et al., 2019)