SecLoop: Security Automation and Verification

Updated 18 December 2025

SecLoop is a security automation framework that integrates LLM-driven defense orchestration with formal verification of hyperproperties in multi-agent workflows.
It automates the complete security lifecycle using techniques like lifecycle automation, group-based RL policy optimization, and parallel validation in realistic 6G environments.
The framework rigorously verifies non-interference in workflows with arbitrary loop structures, ensuring reliable security even under adversarial conditions.

SecLoop is a framework for security automation that enables both advanced LLM-driven defense orchestration in zero-touch networks (ZTNs) and formal verification of information flow properties in multi-agent workflows with loops. SecLoop encapsulates two lines of research: a fully automated architecture for 6G security management (Cao et al., 10 Dec 2025) and an algorithmic verification approach for hyperproperties in parameterized workflows (Finkbeiner et al., 2017). Its distinguishing features include lifecycle automation, group-based RL policy optimization (SA-GRPO), modular integration, parallel validation, and a rigorous approach to verifying non-interference in settings with arbitrary loop structure and agent collaboration.

1. Architectural Principles and Design Goals

SecLoop is engineered for dynamic, heterogeneous, and adversarial environments such as 6G ZTNs. It operationalizes the Observe–Orient–Decide–Act (OODA) loop across the full spectrum from logging to adaptive feedback:

End-to-end lifecycle automation: From intrusion detection system (IDS) alert collection, log summarization, security strategy synthesis via fine-tuned LLMs, parallel execution in virtual environments, and feedback-driven reinforcement learning.
Learnable adaptive strategies: Employs reinforcement learning for iterative improvement of defense policies and automates group-relative evaluation of candidate strategies.
Parallel and practical validation: Deploys category-stratified attack-defense scenarios using real network tools in virtual battlefields to ensure practical executability and reproducibility.
Modular, pluggable integration: Abstracts environment descriptions, tool lists, and controller logic with REST APIs and infrastructure-as-code (IaC) for heterogeneous infrastructure support.
Decidable verification for multi-agent workflows: In its formal verification aspect (Finkbeiner et al., 2017), SecLoop enables automated reasoning about hyperproperties and non-interference even in the presence of unbounded loops and agent collusion.

This architectural synthesis targets two main research challenges: automating the orchestration lifecycle in realistic, adversarial conditions and adapting security policies to evolving, contextualized threats.

2. System Components and Data Flows

SecLoop consists of several interacting modules that together realize scalable and robust security automation:

Log Collector & Summarizer: Ingests raw alerts $\mathcal{L} = \{L_1,\dots,L_n\}$ , applies regex/key-event extraction, and produces compressed summaries $\mathcal{C} = Sr(\mathcal{L})$ for downstream strategy synthesis.
LLM-Based Security Strategy Generator: Uses a fine-tuned Qwen2.5-7B-Instruct model, processing $\mathcal{C}$ , tool configurations $\mathcal{T}_A$ , and environment descriptions $\mathcal{E}$ into a prompt $\mathbf{P}$ and outputting a group $\mathcal{S} = \{S_1,\dots,S_G\}$ of candidate strategies, each specifying parameterized tool invocations.
Security Orchestration Center (SOC): Organizes blue/red team controllers and spins up $N_{\rm env}$ isolated environments using Vagrant + VMware, receiving strategies via API.
Parallel BATTLE-FIELD Environments: Use MITRE ATT&CK-based engines to instantiate realistic multi-stage attack scenarios, confront blue team defenses, and generate reportable feedback:
- $rs_{\rm exe}(S_i)$ : tool execution success
- $rs_{\rm attack}(S_i)$ : attack success/failure
- $rs_{\rm service}(S_i)$ : service availability/continuity
Policy Execution Validator & Feedback Loop: Aggregates multi-dimensional feedback $F_i$ and computes reward signals for reinforcement learning updates.
Data Flow Sequencing: Alerts $\to$ Summarizer $\to$ LLM Agent $\to$ Parallel Battle-Field $\to$ Validator $\to$ SA-GRPO $\to$ LLM update.

Module	Key Function	Interface
Log Collector/Summarizer	Alert compression/preprocessing	REST/API
LLM Security Generator	Defense strategy synthesis	Python API
SOC Controllers	Strategy orchestration	REST/API
BATTLE-FIELD Testbeds	Attack-defense simulation	Middleware
Validator & Feedback	Multi-dimensional scoring	Internal

3. Group Relative RL Optimization: SA-GRPO Algorithm

SecLoop employs the SA-GRPO algorithm, which refines LLM policy parameters through groupwise, multi-dimensional reinforcement:

State-action policy: Each query $q$ serves as a state, strategy $o$ as action, with policy $\pi_\theta(o|q)$ .
Reward decomposition:

$R(q, o) = w_1 R_{\rm format}(o) + w_2 R_{\rm exec}(o) + w_3 R_{\rm eva}(o) - w_4 P(o)$

Where $R_{\rm format}$ checks JSON validity, $R_{\rm exec}$ signals tool error-free execution, $R_{\rm eva}$ scores attack mitigation, and $P(o)$ applies LLM-expert-derived penalties.

Group advantage estimation: For $G$ candidate outputs $\{o_i\}$ per query,

$\hat A_i = r_i - \frac{1}{G} \sum_{j=1}^G r_j$

Clipped surrogate objective: For each token $t$ ,

$\mathcal{J}_{\rm SA-GRPO}(\theta) = \mathbb{E} \Bigg[ \frac{1}{G} \sum_{i=1}^G \sum_{t=1}^{|o_i|} \min(\gamma_{i,t}(\theta)\hat A_i,\, \text{clip}_{i,t}(\theta)\hat A_i) \Bigg]$

Where $\gamma_{i,t}(\theta) = \frac{\pi_\theta(o_{i,t}|q, o_{i,<t})}{\pi_{\theta_{\rm old}}(o_{i,t}|q, o_{i,<t})}$ and clipping parameters are used to enforce safe policy updates.

Training pseudocode: (Algorithm 1 (Cao et al., 10 Dec 2025))

for each training step:
  sample batch D_b
  θ_old ← θ
  for each query q in D_b:
    sample {o_i}_1...G ~ π_{θ_old}(·|q)
    compute rewards r_i = R(q, o_i)
  compute advantages Ĥ_i = r_i – mean(r_j)
  for k = 1...μ:
    θ ← arg max_θ J_SA-GRPO(θ)

4. Formal Workflow Verification in Multi-Agent Settings

SecLoop provides the first automated, decidable technique for verifying non-interference hyperproperties in multi-agent workflows with loops (Finkbeiner et al., 2017):

Workflow language: Parameterized blocks of guarded updates to database relations, with constructs for loops, non-deterministic choice, and unbounded agents.
Operational semantics: Encoding as many-sorted first-order LTL (FOLTL) formulas representing valid infinite executions (including control-flow, state sanity, initialization, and block execution).
Security policy specification: Properties expressed in HyperFOLTL (first-order extension of HyperLTL), capturing non-interference with declassification among agents of different behavioral models (stubborn and causal agents).
Decidability and algorithm: Restricts attention to the Bernays–Schönfinkel–like fragment $3^{*}\forall$ -FOLTL for workflows satisfying the "non-omitting" property, ensuring the verification problem remains decidable via translation to bounded LTL satisfiability.
Implementation (NIWO tool): Three-phase verification—encoding, fragment/prenex check, LTL conversion and solving.

Verification Step	Description
Encoding	Parse workflow/property, build FOLTL model
Fragment Check	Confirm $3^{*}\forall$ -FOLTL membership, skolemize
LTL Solve	Translate to LTL, call solver (AALTA), return result

5. Experimental Results and Real-World Performance

Empirical evaluations of SecLoop and SA-GRPO on five security benchmarks (CIC-IDS2017/2018, UNSW-NB15, CCDC-2018, AutoAttack) demonstrate state-of-the-art results:

Strategy accuracy gains: +41.6% over xNIDS, +50.0% over SAGE for comprehensive testbeds.
LLM comparison: SecLoop (7B) achieves +66.9%/+32.9%/+1.2% improvement relative to Grok-3β, Gemini-2.5, and GPT-4.1 respectively.
RL baselines: SA-GRPO outperforms DQN/DDQN/PPO/KTO/GRPO by 26.7%/22.7%/18.6%/14.7%/10.3%.
Reward ablation: Removing execution/evaluation rewards, penalty, or format checks diminishes overall accuracy by 26.9%, 10.0%, and 3.1% respectively.
Parallelization: Larger group size yields higher accuracy; $G=7$ is empirically optimal.
Edge deployment: On Jetson AGX Orin and ORIN NX, real-world accuracy reaches 91.35%, closely matching simulation (92.71%), with SA-GRPO outperforming PPO/KTO/GRPO by significant margins.
Formal verification (NIWO): Practical workflows (conference, notebook, grading) analyzed within seconds to minutes, scaling gracefully with agents and workflow arity.

Benchmark	Metric	SecLoop (7B)	Baselines
xNIDS/SAGE	Accuracy Gain	+41.6%/+50.0%	-
Grok-3β/Gemini-2.5	Accuracy Gain	+66.9%/+32.9%	-
Edge (Orin)	Accuracy	91.35%	-
RL (SA-GRPO)	Outperform	+26.7%...	DQN/DDQN/...

6. Limitations and Prospective Enhancements

SecLoop’s current boundaries and planned advancements are as follows:

Limitations:
- Maintenance of virtual testbeds and IaC templates necessary as network topologies change.
- High resource requirements, including GPU clusters and parallel VM infrastructure.
- Current focus is reactive detection and mitigation; proactive threat discovery mechanisms are absent.
- In formal verification, the non-omitting restriction and bounded causal agents are required for decidability. Dropping either entails undecidability.
Future directions:
- Implementation of online self-evolving fine-tuning from live traffic.
- Development of Guardian Models for runtime output isolation and formal verification against misconfigurations.
- Model distillation techniques to enable efficient LLM deployment on resource-constrained edge devices.
- Integration of fuzz-testing and code analysis capabilities for proactive vulnerability identification.
- Extension of workflow verification methods to richer loops and scheduling policies, potentially leveraging SMT solvers for more expressive fragments.

A plausible implication is that as ZTN environments and collaborative workflows grow in complexity, frameworks like SecLoop provide a foundation for scalable, safe, and adaptive security orchestration as well as automatized formal verification of hyperproperties. These advances facilitate both defense operation and policy assurance across the next generation of network systems (Cao et al., 10 Dec 2025, Finkbeiner et al., 2017).