AutoBreach: Adaptive Security Breach Framework

• AutoBreach is a suite of adaptive frameworks that automate risk identification, exploitation, and defense in cyber-physical systems, connected vehicles, and AI models.
• It employs novel methods like black-box jailbreaking, two-stage optimization, and dependency-guided ensembles to achieve high attack success rates and robust defenses.
• Its applications span from vehicular systems to enterprise networks, enhancing incident response, threat intelligence, and privacy protection with scalable automated techniques.

AutoBreach encapsulates a diverse set of automated, adaptive, and systemic frameworks, methods, and threat paradigms that identify, exploit, or defend against security, privacy, and safety breaches across connected vehicles, cyber-physical systems, cloud-native applications, and artificial intelligence models. The term covers both real-world vehicular failures (e.g., brake-by-wire and botnet attacks), large-scale cyberattacks and data breaches modeled as man-made catastrophes, automated cyber triage and incident response, as well as universal adaptive methods for jailbreaking LLMs via black-box adversarial optimization and dependency-guided ensemble strategies.

1. Conceptual Foundations and Risk Modeling

AutoBreach frameworks emerge from disciplines that include automotive safety science, catastrophe risk modeling, adversarial computing, and AI security. In automotive systems, risk-significant scenarios are identified by backtracking from a hazardous event (e.g., collision) using probabilistic evolution via a Markov/Cell-to-Cell Mapping Technique (CCMT) under stochastic hardware failure modeling (Hejase et al., 2018). In the cyber domain, data breaches are modeled as man-made catastrophes, requiring multi-level modular risk analysis:

• Exposure: Quantity and structure of sensitive data at risk
• Hazard/Threat: Intensity and frequency of breach events
• Vulnerability: Susceptibility given system defenses
• Damage/Loss: Quantitative event size and economic/social impact Compound process models integrate frequency (negative binomial) and severity (Pareto distribution) to forecast aggregate losses, emphasizing the emergence of heavy-tailed risk profiles (Wheatley et al., 2019).

2. Automated Breach and Jailbreak Methods

In the field of LLMs, AutoBreach represents a novel methodology for black-box jailbreaking characterized by universality, adaptability, and query efficiency (Chen et al., 30 May 2024). The core workflow involves:

• Wordplay-Guided Mapping Rule Sampling: Automatic generation of universal text mapping rules via LLM-based reasoning
• Sentence Compression and Chain-of-Thought Transformation: Techniques promoting clarity and minimizing misinterpretation in adversarial prompts
• Two-Stage Optimization: Pre-optimization with a Supervisor LLM to select robust mapping rules before querying the target model, followed by iterative refinement based on output scoring AutoBreach demonstrates high attack success rates (>80%) on proprietary models (e.g., GPT-4 Turbo) with fewer than 10 queries per attack.

Complementary to this approach, ensemble frameworks (AutoAttack, AutoDefense, AutoEvaluation) exploit directed acyclic graph (DAG) dependency analyses to combine genetic algorithm–based and adversarial generation–based attacks, leveraging inter-method dependencies to maximize efficiency and coverage of adversarial and defensive strategies (Lu et al., 6 Jun 2024). These frameworks distinguish successful jailbreaks, hallucinations, and alignment refusals for rigorous response assessment.

AutoDAN-Turbo extends this paradigm with lifelong strategy autotuning: discovered jailbreak strategies are automatically distilled via comparative scoring and summarization, stored in a vector-embedded strategy library, and retrieved by context similarity for plug-and-play transfer and continual self-improvement (Liu et al., 3 Oct 2024). Performance metrics surpass previous baselines with >88% success rates on frontier LLMs.

3. Cyber-Physical Systems: Vehicular and Network Breach Scenarios

AutoBreach in vehicular domains includes adversarial supply-chain compromises, as seen in attacks on Adaptive Cruise Control (ACC) systems (Gunter et al., 2021). Random Deceleration Attacks (RDAs) injected on the vehicle CAN bus can degrade mean commuter speed by over 7%, reduce throughput by up to 3%, and incur economic costs exceeding 300 USD/km·hr without being detected by GPS-based or CAN-level anomaly detectors, unless attack magnitude crosses stealthiness thresholds.

AntibotV applies multilevel behavioral monitoring of network and in-vehicle communications for connected cars (Rahal et al., 3 Jul 2024). Detection involves machine learning classifiers (notably decision trees via ID3) trained on features such as flow duration, inter-packet interval, CAN ID, and byte statistics, achieving >97% detection rates and <0.14% false positives, surpassing prior single-level botnet detectors.

In enterprise networks, automated incident response and cyber triage systems (e.g., Hopper, PrioTracker, AirTag, ProvDetector) reconstruct causal graphs from host/network logs, using metrics combining rareness and fanout to score suspicious event chains (Rao, 30 Apr 2024). PrioTracker’s scoring, saliency-based provenance partitioning (NoDoze), log compression (SEAL), and interpretability modules (AirTag) streamline analyst workload and enable unified breach root-cause isolation.

4. Automated Threat Intelligence: Alert Tree Reconstruction and Prioritization

AutoCRAT exemplifies automated cumulative reconstruction of alert trees from intrusion detection alerts (Ficke et al., 17 Sep 2024). The system constructs alert graphs, indexed path collections, and recombined tree structures to quantify threat via geometric mean metrics of alert diversity and volume:

• Threat Score: TS(A) = √(D × N), with D = distinct alert types and N = total alerts Threat prioritization leverages endpoint, path, and tree-level rankings for cyber triage, with experimental validation confirming scalable full coverage and efficient retrieval, making AutoCRAT suitable as the backbone of automated breach response platforms.

5. Privacy Vulnerabilities in Connected and Autonomous Systems

AutoBreach risks in connected vehicle privacy arise when minimal driving telemetry (cornering events, average speed, total time) are sufficient to reconstruct driver paths via directed graph algorithms and popularity-weighted route finding, enabling inference of sensitive personal patterns (Kaplun et al., 2019). Formal expressions include: $$W([\nu_i, …, \nu_k]) = \frac{1}{k-1} \sum_{j=i}^{k-1} w(e_{v_j, v_{j+1}})$$

$d(\text{Path}, v) < W(\text{Path}, v) \times A_t$

Mitigation calls for differential privacy, reduced data granularity, anonymization, and cryptographic multiparty computation.

Furthermore, gaps in GDPR compliance among major automakers (BMW, Mercedes-Benz) expose additional risk. Compliance rates of 32% (BMW) and 23% (Mercedes) leave vulnerability indices V ≈ 0.68 and V ≈ 0.77, meaning substantial attack surfaces persist (Bella et al., 18 Oct 2024). Modelled AutoBreach risk: $$\text{Risk}_{\text{AutoBreach}} = \alpha \cdot \frac{(1 - C_{\text{score}}) \cdot D}{S}$$ where α = attack sophistication, D = sensitive data processed, S = strength of security measure.

6. Impact, Alignment Regression, and Research Outlook

Data from recent studies demonstrate that the underlying reasoning and planning capacities of LRMs (e.g., DeepSeek-R1, Grok 3, Gemini 2.5) enable autonomous multi-turn jailbreaks with ASR ≈ 97.14%, establishing alignment regression risks for the next generation of frontier models (Hagendorff et al., 4 Aug 2025). This effect is multiplicative: a single powerful LRM can compromise alternative models with only benign conversational framing and role-play escalation.

Collectively, AutoBreach methodologies highlight an urgent need for:

• Further alignment and defense hardening in AI models to resist autonomous adversarial exploitation
• Incident response system advances combining real-time causality tracing, scalable graph analytics, and prioritization heuristics
• Systemic privacy and regulatory compliance enhancements in automotive and connected systems
• Multimodal extension, continuous feedback incorporation, and the development of detection heuristics tailored to adaptive adversarial methods.

These directions define the ongoing evolution of AutoBreach as both a threat paradigm and a suite of technical, analytic, and operational solutions relevant across domains.