arXiv subjects

Frederico Araujo

Publications and source records attributed to Frederico Araujo.

8 recordsLinked to original sources

arXiv2026

Rethinking Software-Defined Networking Link Discovery with Dynamic Randomization

Software-defined networking (SDN) separates the control and data planes, enabling programmable, centralized network management. A core SDN service is topology discovery --- a periodic process that identifies network links. However, our analysis of five open-source SDN controllers and ten discovery protocols shows that all remain vulnerable to at least one form of link-fabrication attack. The common root cause is their reliance on traditional Link Layer Discovery Protocol (LLDP) packets, whose static identifiers expose their discovery purpose and attract adversaries to exploit them. We propose ChameleonDisc, a dynamic link-discovery protocol that simultaneously prevents and detects topology-poisoning attacks by eliminating this root cause. Our key insight is that an SDN controller can infer topology without embedding meaningful information in discovery packets. ChameleonDisc removes targetable static LLDP signatures and employs a moving-target defense that combines decoy, obfuscation, and camouflage techniques instantiated dynamically at runtime to prevent topology poisoning and detect manipulation. We implement ChameleonDisc on OpenDaylight and demonstrate effectiveness against all identifier-based topology-poisoning attacks. On a 252-link topology, median convergence is 1.37--4.53\,s for legitimate link changes and 4.06\,s for malicious relay detection, with 13.0 percentage points of additional mean CPU and negligible retained-heap difference. Across topologies up to 816 links, ChameleonDisc provides a tunable security--performance trade-off; expanding discovery intervals reduces CPU and mapping state at the cost of increased attack-detection latency.

cs.NI↗

arXiv2026

AEGIS: Preventing Cross-Domain Resource Abuse in MCP

The Model Context Protocol (MCP) is an open source JSON-RPC protocol that standardizes how large language models (LLMs) interact with external systems through programmatic functions known as tools. Attackers or malicious agents can exploit certain modalities of these MCP tools to degrade the overall quality of service of agent-based applications. For example, an agent may request an excessively large search radius or very long videos, overloading backend systems and potentially causing slowdowns or denial-of-service. Each modality including text, images, video, and location introduces distinct vectors for resource abuse, complicating the development of consistent mitigation strategies. Moreover, multimodal and crossdomain tools expose diverse request schemas and parameters, making it difficult to define policies that are both generalizable and precise enough to enforce meaningful resource constraints. In this paper, we present AEGIS, a policy enforcement component that enables administrators to define fine-grained safeguards against resource abuse across heterogeneous MCP tools and modalities. AEGIS leverages the reasoning capabilities of large language models to analyze, categorize, and normalize diverse tool invocations into a unified, policy-friendly representation accessible to security practitioners. Integrated with the Open Policy Agent and the ContextForge AI Gateway, AEGIS detects and mitigates abusive behaviors while preserving the flexibility of MCP-based agent ecosystems.

cs.CR↗

arXiv2026

eMicro: Real-Time Multi-Hop Access Control for Microservices with eBPF

Modern cloud applications often comprise thousands of microservices whose interactions form complex request paths. Traditional inter-service access control restricts individual service-to-service requests, but fails to prevent multi-hop attacks, where each hop appears legitimate yet the overall path violates security intent. This gap leaves systems exposed to unauthorized access and data exfiltration. In this paper, we present eMicro, a path-aware defense system for microservices that prevents such attacks while remaining efficient and deployable. eMicro enforces real-time multi-hop access control through three key techniques: (1) history-based access control extended to capture service invocation sequences; (2) security policies encoded as efficient deterministic finite automaton (DFA), supporting constant-time lookups and compact label propagation; (3) eBPF-based in-kernel request tracing for transparent, low-overhead enforcement without code changes. Evaluations on DeathStarBench and production cloud traces from Uber, Alibaba, and ByteDance, covering 12 million request workflows and thousands of services, demonstrate the scalability of eMicro. eMicro performs policy checks in 1 microsecond, stores 50 million policies in only 100 MB, and reduces propagation overhead by 90% with negligible runtime impact. These results show that eMicro delivers scalable and efficient protection against multi-hop attacks, making it practical for deployment in large-scale microservice environments.

cs.CR↗

arXiv2026

Lessons from Penetration Tests on Large-Scale Agent Systems

As AI systems gain increasing autonomy and execution capability, the number of discovered security vulnerabilities continues to rise. However, many of these vulnerabilities are not fundamentally novel, but instead reflect recurring classes of weaknesses long observed in prior computing systems. Execution-capable AI agents are effectively unbounded, self-modifying programs that interact extensively with multiple layers of the computing stack. This broad interaction surface imposes a significant security burden on developers, who must reason about and secure complex cross-layer behaviors. Prior research has primarily focused on vulnerabilities in open-source agents and agent frameworks. In contrast, it remains unclear whether proprietary agent systems -- developed under stricter coding standards and formal review processes -- exhibit similar security weaknesses. In this paper, we present findings from two penetration tests conducted in 2025 against proprietary agent products and evaluate whether the security posture of AI agents has improved since these assessments.

cs.CR↗

arXiv2024

Manipulating OpenFlow Link Discovery Packet Forwarding for Topology Poisoning

Software-defined networking (SDN) is a centralized, dynamic, and programmable network management technology that enables flexible traffic control and scalability. SDN facilitates network administration through a centralized view of the underlying physical topology; tampering with this topology view can result in catastrophic damage to network management and security. To underscore this issue, we introduce Marionette, a new topology poisoning technique that manipulates OpenFlow link discovery packet forwarding to alter topology information. Our approach exposes an overlooked yet widespread attack vector, distinguishing itself from traditional link fabrication attacks that tamper, spoof, or relay discovery packets at the data plane. Unlike localized attacks observed in existing methods, our technique introduces a globalized topology poisoning attack that leverages control privileges. Marionette implements a reinforcement learning algorithm to compute a poisoned topology target, and injects flow entries to achieve a long-lived stealthy attack. Our evaluation shows that Marionette successfully attacks five open-source controllers and nine OpenFlow-based discovery protocols. Marionette overcomes the state-of-the-art topology poisoning defenses, showcasing a new class of topology poisoning that initiates on the control plane. This security vulnerability was ethically disclosed to OpenDaylight, and CVE-2024-37018 has been assigned.

cs.CR↗

arXiv2022

Using Deception in Markov Game to Understand Adversarial Behaviors through a Capture-The-Flag Environment

Identifying the actual adversarial threat against a system vulnerability has been a long-standing challenge for cybersecurity research. To determine an optimal strategy for the defender, game-theoretic based decision models have been widely used to simulate the real-world attacker-defender scenarios while taking the defender's constraints into consideration. In this work, we focus on understanding human attacker behaviors in order to optimize the defender's strategy. To achieve this goal, we model attacker-defender engagements as Markov Games and search for their Bayesian Stackelberg Equilibrium. We validate our modeling approach and report our empirical findings using a Capture-The-Flag (CTF) setup, and we conduct user studies on adversaries with varying skill-levels. Our studies show that application-level deceptions are an optimal mitigation strategy against targeted attacks -- outperforming classic cyber-defensive maneuvers, such as patching or blocking network requests. We use this result to further hypothesize over the attacker's behaviors when trapped in an embedded honeypot environment and present a detailed analysis of the same.

cs.GT↗

arXiv2021

Evidential Cyber Threat Hunting

A formal cyber reasoning framework for automating the threat hunting process is described. The new cyber reasoning methodology introduces an operational semantics that operates over three subspaces -- knowledge, hypothesis, and action -- to enable human-machine co-creation of threat hypotheses and protective recommendations. An implementation of this framework shows that the approach is practical and can be used to generalize evidence-based multi-criteria threat investigations.

cs.CR↗

arXiv2021

Towards an Open Format for Scalable System Telemetry

A data representation for system behavior telemetry for scalable big data security analytics is presented, affording telemetry consumers comprehensive visibility into workloads at reduced storage and processing overheads. The new abstraction, SysFlow, is a compact open data format that lifts the representation of system activities into a flow-centric, object-relational mapping that records how applications interact with their environment, relating processes to file accesses, network activities, and runtime information. The telemetry format supports single-event and volumetric flow representations of process control flows, file interactions, and network communications. Evaluation on enterprise-grade benchmarks shows that SysFlow facilitates deeper introspection into attack kill chains while yielding traces orders of magnitude smaller than current state-of-the-art system telemetry approaches -- drastically reducing storage requirements and enabling feature-filled system analytics, process-level provenance tracking, and long-term data archival for cyber threat discovery and forensic analysis on historical data.

cs.CR↗