PACEagent: Modular Analytics & Cybersecurity

• PACEagent is a modular agent framework combining enterprise prescriptive analytics with cyber-exploitation benchmarking for autonomous, multi-phase operations.
• It employs LLM-powered modules such as function calling, perception, and memory to facilitate causal analysis, prescriptive policy learning, and realistic penetration testing.
• The framework demonstrates domain adaptability and rigorous evaluation through prompt tuning, dynamic context management, and validated benchmarking across varied attack scenarios.

PACEagent refers to two distinct agent frameworks known for their autonomous operation and modular design in the domains of enterprise prescriptive analytics and cyber-exploitation benchmarking. In both contexts, PACEagent is engineered to either help end-users perform complex causal and prescriptive tasks or rigorously emulate the multi-phase workflow of a human penetration tester in realistic cybersecurity scenarios. Below, each incarnation’s architecture, use cases, and technical dimensions are detailed.

1. Architectural Design and Modular Components

PACEagent, in the enterprise context (Orderique et al., 29 Jul 2024), is developed as PrecAIse—a domain-adaptable agent underpinned by an LLM-powered modular architecture. The agent consists of several key modules:

• Function Calling Module: Routinely maps user requests (e.g., show_causal_effect, run_optimize) to backend causal/prescriptive functions.
• Perception Modules: Incorporate an intent classifier and multiple parameter (“column”) extractors, with prompt tuning and parameter-efficient fine-tuning for accurate parsing of conversational input.
• Memory Module: Maintains a dynamic record of user dialogue, including extracted parameters, supporting multi-turn interaction.
• Conversational Module: Built on a Sparse Mixture of Experts model (Mixtral 8x7B Instruct), facilitating natural-language responses and “thought injection” for prompt follow-ups.

In the cyber-exploitation context (Liu et al., 13 Oct 2025), PACEagent features three primary components:

• LLM Core: Handles mission comprehension, strategic planning, and generates phase-managed instructions to coordinate attack sequences.
• Tool Module: Uses a tool router to invoke both local (Linux utilities) and external cybersecurity programs, mediated via a Model Context Protocol (MCP).
• Memory Module: Preserves all agent thoughts, actions, and observations; employs auxiliary LLMs for log summarization to support long-horizon operations.

Both frameworks are orchestrated via server processes that support audit logging, function exposure, and seamless phase transition.

2. Natural Language Interfaces and Interactivity

In PrecAIse, the Natural Language User Interface (NLUI) wraps sophisticated analytics pipelines in a conversational front-end accessible to non-experts. Its features include:

• Text-based Query Input: Users can pose arbitrary queries in natural language.
• Multimodal Output: Responses include text explanations alongside visual elements—plots, bar charts, tree diagrams—generated on demand.
• Parameter Management: Automated checking for missing parameters; the agent issues follow-up questions using “thought injection.”
• Dynamic Context Memory: Short-term memory tracks prior exchanges, informing subsequent prompts for improved continuity and adaptivity.

The cyber-exploitation PACEagent’s agent server enables phased cyber operations, logging actions and ensuring context preservation for realistic penetration testing emulation.

3. Domain Adaptation and Generalization

PrecAIse is equipped with a domain generalization pipeline requiring only a dataset (CSV) and metadata—specifying dataset titles, action/outcome variables, and column descriptions. The system:

• Covariate Selection: Automated selection of relevant columns.
• Template Filling: Generation of domain-specific training samples and configuration files.
• Prompt Regeneration: Automated synthesis of domain-adapted system prompts, and re-training of classifier and extractors.

Minimal manual intervention is needed for new domains (e.g., airline pricing, bank marketing), supporting enterprise scalability and rapid context customization.

PACEagent for cyber-exploitation adapts to increasingly complex environments, from single-host CVE exploitation to multi-host blended, chained, and defended scenarios. Scenario definitions and complexity are auto-configured via PACEbench protocols.

4. Scenario Coverage and Functional Capabilities

PACEagent’s cyber-exploitation role is evaluated using four benchmark scenarios (Liu et al., 13 Oct 2025):

Scenario	Description	Complexity
A-CVE	Exploits single, known CVEs (“human pass rate” annotated)	Low–Moderate
B-CVE	Blended multi-host, compromised/benign mix	Moderate
C-CVE	Chained, multi-stage exploits (pivot, escalate)	High
D-CVE	Hosts behind robust WAF defenses	Very High

PrecAIse supports:

• Causal Analysis: Estimation of average/conditional causal effects (e.g., $\Delta = E[Y | A=a_1] - E[Y | A=a_2]$).
• Prescriptive Policy Learning: Construction of interpretable, constraint-aware trees for policy optimization (e.g., resource-bounded telemarketing strategies).
• Dynamic Interactive Workflows: Stepwise parameter gathering via chat for accurate function execution.

PACEagent’s attack pipeline comprises reconnaissance, host analysis, exploit orchestration, and WAF evasion attempts.

5. Evaluation Metrics, Performance, and Limitations

PACEbench employs a unified scoring metric as a weighted sum across scenarios (Pass@5 criterion):

$$\text{BenchScore} = A_{\text{score}} \cdot w_A + B_{\text{score}} \cdot w_B + C_{\text{score}} \cdot w_C + D_{\text{score}} \cdot w_D$$

with $w_A = 0.2$, $w_B = 0.3$, $w_C = 0.3$, $w_D = 0.2$.

Findings include:

• Performance Degradation: State-of-the-art LLMs (e.g., Claude-3.7-Sonnet scored 0.241 overall) handled A-CVE adequately but failed at increasingly complex scenarios, especially D-CVE where no model bypassed WAFs.
• Token Consumption: PACEagent uses 28% more tokens than comparators (CAI framework), justified by improved operational accuracy.
• Error Sources: Difficulties maintaining long-term context and executing chained reasoning; PrecAIse’s initial instantiations suffered misclassification (e.g., price–market pair confusion) and hallucinations, addressed by prompt tuning and “thought injection.”

6. Technical Challenges, Solutions, and Impact

Technical challenges in PrecAIse included rigid, few-shot in-context learning and parameter misclassification, mitigated by:

• Prompt Tuning for Classifiers and Extractors: Improving accuracy from ~64% to ~95% on test cases.
• Automated Pipeline for Domain Adaptation: Creation of system prompts and retraining for new business contexts.
• Conversation Management: “Thought injection” for robust two-way interaction and parameter acquisition.

PACEagent for cyber-exploitation highlights active limitations:

• WAF Resistance: No current LLM agent bypassed modern web defenses.
• Long-Horizon Reasoning: Persistent challenge in chained attacks and multi-host navigation.

Impact includes dramatically lower technical barriers for enterprise analytics deployment and providing a rigorous, trustworthy cyber-exploitation evaluation platform for AI model development.

7. Prospects and Future Directions

PrecAIse’s roadmap includes expanding support for more prescriptive tools, investigating additional parameter-efficient fine-tuning approaches, and refining conversation/memory modules for natural, multi-turn dialog in complex scenarios.

PACEagent’s future directions highlight the need for improved long-term reasoning, better autonomous navigation in defended environments, and enhanced modular safeguards. It serves as a baseline for trustworthy AI system assessment, informing both operational control strategies and proactive penetration/vulnerability remediation research.

A plausible implication is that continued innovation in conversation management, tool orchestration, and memory summarization are essential for robust, versatile agent deployment across enterprise and cybersecurity domains.