SecLog-Bench: A Scalable Benchmark for Cybersecurity Log Reasoning with Scaffolded Evidence
SecLog-Bench introduces a benchmark for post-detection cybersecurity log reasoning that requires LLMs to infer attack progress from scaffolded multi-source evidence and produce structured, evidence-referenced reasoning chains. The authors construct 29 reproducible attack scenarios spanning CVE exploits and multi-hop attacks, aligning host audit logs, application logs, and network traffic into stage-labeled evidence windows. Evaluation of eight mainstream LLMs reveals modest attack-stage prediction (best accuracy 44.44%) and unreliable reasoning (best SRF1 54.87%), demonstrating that final-label accuracy alone misrepresents true model capability.
The paper makes a solid contribution by shifting cybersecurity log analysis from binary detection to structured, window-level attack-stage reasoning with explicit reasoning evaluation. The construction pipeline is well-engineered, with reproducible scenarios, lightweight auxiliary indices for cross-source alignment, and a human-validated reasoning-aware evaluation protocol. However, the benchmark's value is somewhat constrained by its small evaluation set (216 windows) and reliance on simulated rather than operational environments.
The problem formulation is sharp. Defining $\mathcal{Y} = \{\text{preparation}, \text{execution}, \text{result}, \text{stage\_unclosed}\}$ over evidence windows rather than full kill-chain stages grounds the task in visible evidence. The reasoning-aware evaluation protocol is the strongest technical element: metrics like $\mathrm{SRP} = \frac{1}{N}\sum_{i=1}^{N} s(\ell_i)$, SCC, and SRF1 separate evidence-supported claims from label guessing. The ablation in Fig. 9 is convincing, showing that scaffolded, compressed multi-source evidence significantly outperforms raw logs.
With only 216 human-reviewed windows—54 per label—claims about LLM capability on cybersecurity log reasoning are extrapolated from a narrow slice of synthetic attacks. There is also a risk of circularity in the reasoning evaluation: while the authors validate assistant judgments against human reviewers ($\kappa = 0.78$), the intermediate scoring factors in $s(\ell_i) = g_i q_i^{\mathrm{sup}} q_i^{\mathrm{ref}} p_i^{\mathrm{over}} q_i^{\mathrm{stage}} p_i^{\mathrm{bound}}$ rely on fixed ordinal mappings and LLM-based assistants whose reliability on security logs is itself unproven. The prediction–reasoning mismatch findings, while important, are somewhat expected given the difficulty of the task and the small sample size.
The evidence supports the core claim that current LLMs struggle with attack-stage reasoning. Table IV shows Claude Sonnet 4.6 at 44.44% accuracy and 45.07% Macro-F1, while Table V reveals GPT-5.4 achieves the highest SRF1 of 54.87% despite middling accuracy, corroborating the misalignment thesis. Comparisons to prior datasets in Table III are fair: the authors correctly note that existing resources lack reasoning chains or extensible scenario generation. The ablation distinguishing audit-only from multi-source evidence (Fig. 9b) empirically justifies the multi-source design, though the full-view diagnostic metrics ($\mathrm{SRP}^{\dagger}$, $\mathrm{SCC}^{\dagger}$) are computed against the full evidence rather than the compressed view, which complicates interpretation.
The paper scores well on reproducibility for a benchmark study. The authors open-source code and data, specify exact model versions and API dates, and document inference settings (temperature = 0, top-p = 1). The simulation platform uses Docker and reusable runtime adapters, covering 29 scenarios with per-run completion within 70 minutes. The main barrier to independent reproduction is access to the proprietary models and the computational cost of locally deploying 32B-parameter models on RTX 5090 GPUs. Additionally, the human-review bottleneck for reference chain confirmation means scaling beyond 216 windows would require substantial manual effort, limiting the benchmark's immediate extensibility despite the scalable log-generation backend.
Large language models (LLMs) have shown strong potential for cybersecurity, leading to growing interest in LLM-based automated cybersecurity log analysis. However, current datasets (1) are limited in scale; (2) rely mainly on single-source evidence; and (3) have only binary or multi-class attack labels, without dense annotations of the reasoning process. To address these limitations, SecLog-Bench has three key features. First, we build an extensible simulation platform that covers 29 attack scenarios, supports repeated execution and the integration of new scenarios, and enables scalable generation of cybersecurity logs with attack-stage labels. Second, we use multi-source logs, including audit logs, application logs, and network traffic, to jointly analyze cyber attacks. Third, we annotate each sample with the essential context for cybersecurity log reasoning and a structured step-by-step evidence chain. We evaluate eight mainstream LLMs on this benchmark for post-detection attack-progress reasoning. Results show that, when provided with scaffolded evidence, current LLMs exhibit modest capability in attack-progress reasoning, with the best model achieving only 44.44% accuracy and 45.07% Macro-F1. Their reasoning remains unreliable; i.e., the highest Stage Reasoning F1 (SRF1) score is only 54.87%. Moreover, even correct predictions may be accompanied by flawed reasoning, and reasoning quality is often misaligned with prediction accuracy.
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