From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability

• URL: http://arxiv.org/abs/2606.05252v1
• Date: Wed, 03 Jun 2026 14:26:25 GMT
• Title: From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability
• Abstract summary: Security teams simulate attacks against their own systems to check whether their monitoring would catch a real intruder.<n>A human bridges that gap by hand, reading each finding and writing the corresponding Sigma rule.<n>We show this translation can be partially automated when probes are drawn from a locked corpus.
• Score: 51.56484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder. These Breach-and-Attack-Simulation (BAS) tools surface findings, but the security information and event management (SIEM) systems that watch production need detection rules -- and today a human bridges that gap by hand, reading each finding and writing the corresponding Sigma rule (a vendor-neutral detection format). We show this translation can be partially automated when probes are drawn from a locked corpus, so each finding carries a stable identifier back to the originating probe. We describe a deterministic synthesis function that maps each finding to a starter Sigma rule through a small template library (N=23, indexed by categories from the OWASP LLM and Web Top 10), with a back-reference to the originating finding and its MITRE ATT&CK technique. On two locked corpora (17-probe LLM, 23-probe Web), every bypassed-probe finding yields a starter rule, and all 17/17 emitted rules parse and convert to Splunk and Elasticsearch backends. Replayed through a live OpenSearch SIEM, the LLM rules fire on 30% of a held-out AdvBench subset and 14% of HarmBench at 7.7% false positives on a benign baseline; the Web side is validated structurally, not against a held-out attack set. The contribution is a verifiable, byte-stable path from BAS finding to operator-deployable starter rule, re-derivable from the published corpus and template library alone -- trading the breadth of LLM-generative methods for exact reproducibility and a typed traceback from any fired alert to the originating probe.

Related papers

• LMSM: LLM Security Framework Inspired by Linux Security Modules [56.93644694627783]
  Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them.<n>We present Language Model Security Modules (LMSM), a security framework that adapts the separation behind Linux Security Modules (LSM) to LLM serving.
  arXiv  Detail & Related papers  (2026-08-26T12:13:05Z)
• CyberLLM: A Multi-Agent LLM Framework for Autonomous Detection and Guarded Response in Automotive Cybersecurity [22.68559728326395]
  CyberLLM is a framework that autonomously detects vulnerabilities and executes remediations under a formal, runtime safety guard.<n>Decision agents aggregates findings, tags them with a human-centric asset taxonomy, and selects a tiered response.<n>A symmetric attack pipeline generates and replays exploits so both sides can be exercised on the same scenarios.
  arXiv  Detail & Related papers  (2026-08-06T23:39:13Z)
• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture [0.3823356975862005]
  We describe an architecture pairing generation with verification, resting on three principles.<n>This determinism-where-possible split, organized as an integrity-gate taxonomy, is the core contribution.<n> MedSci Skills is an open-source toolkit of 43 skills with a 21-detector deterministic tier, evaluated on three public-dataset pipelines.
  arXiv  Detail & Related papers  (2026-06-08T13:51:04Z)
• GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks [1.7816843507516946]
  Generative Thread Intelligence (GenTI) is a benchmark for automatic generation of IDPS rules targeting unseen attacks.<n>GTI aggregates over 150k detection and prevention rules from Snort, Suricata, Emerging Threats, as well as 50k YARA, each annotated with protocol behavior, payload signatures, contextual relationships, mappings to Cyber Threat Intelligence (CTI)<n>Our GenTI instantiation achieves a composite rule-quality score of 89.4%, with 94.8% CTI coverage, improving unseen attacks detection from 45% to 87.4% and reducing the false-positive rate from 8.5%
  arXiv  Detail & Related papers  (2026-06-04T08:19:52Z)
• FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences [4.351505522514463]
  We introduce instance-level fingerprinting, a regulator-oriented paradigm that distinguishes configurations of the same Large Language Model.<n>Our method FLIPS, exploits biases in generated binary random sequences to reach 96% (closed-set) and 90% (open-set) identification accuracy across 237 model instances.<n>This shows that instance-level fingerprinting is both necessary for regulation and practically feasible.
  arXiv  Detail & Related papers  (2026-06-02T08:39:50Z)
• Refusal Evaluation in Coding LLMs and Code Agents: A Systematic Review of Thirteen Malicious-Code Prompt Corpora (2023-2025) [0.0]
  Existing surveys treat code security, jailbreak taxonomy, or vulnerability detection as the central object and mention these corpora only in passing.<n>This paper treats the prompt datasets themselves as the unit of analysis.<n>The synthesis surfaces three recurring methodological gaps.
  arXiv  Detail & Related papers  (2026-05-19T18:05:51Z)
• Beyond Input Guardrails: Reconstructing Cross-Agent Semantic Flows for Execution-Aware Attack Detection [32.301679396929536]
  We propose SysName, a framework that shifts the defensive paradigm from static input filtering to execution-aware analysis.<n>SysName synthesizes fragmented operational primitives into contiguous behavioral trajectories, enabling a holistic view of system activity.<n> Empirical evaluations demonstrate that SysName effectively detects over ten distinct compound attack vectors, achieving F1-scores of 85.3% and 66.7% for node-level and path-level end-to-end attack detection, respectively.
  arXiv  Detail & Related papers  (2026-03-04T01:59:16Z)
• When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift [0.0]
  We present a comprehensive analysis using a benchmark of 18 datasets spanning harmful requests, jailbreaks, indirect prompt injections, and extraction attacks.<n>We propose Leave-One-Dataset-Out (LODO) evaluation to measure true out-of-distribution generalization.
  arXiv  Detail & Related papers  (2026-02-15T14:21:43Z)
• The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search [58.8834056209347]
  Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs.<n>We introduce the Correlated Knowledge Attack Agent (CKA-Agent), a dynamic framework that reframes jailbreaking as an adaptive, tree-structured exploration of the target model's knowledge base.
  arXiv  Detail & Related papers  (2025-12-01T07:05:23Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Defending against Indirect Prompt Injection by Instruction Detection [109.30156975159561]
  InstructDetector is a novel detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks.<n>InstructDetector achieves a detection accuracy of 99.60% in the in-domain setting and 96.90% in the out-of-domain setting, and reduces the attack success rate to just 0.03% on the BIPIA benchmark.
  arXiv  Detail & Related papers  (2025-05-08T13:04:45Z)
• Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
  Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
  arXiv  Detail & Related papers  (2025-02-10T09:15:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.