arXiv subjects

Samuel Ouya

Publications and source records attributed to Samuel Ouya.

3 recordsLinked to original sources

arXiv2026

GapFuzz: Cross-Plane Divergence Fuzzing for Distributed SDN Controllers

Distributed Software-Defined Networking (SDN) clusters replicate flow state asynchronously between a master node and its backups, leaving a window during which two backup nodes can each commit a contradictory rule, the master can serialize both into the data plane, and the kernel datapath can latch onto an action that no node believes authoritative. Existing SDN fuzzers miss this fault: they confine their oracle to the control plane, target a single controller, or do not steer concurrency to provoke replication races. We present GapFuzz, a stateful concurrency fuzzer for distributed SDN clusters. GapFuzz injects pairs of contradictory Northbound requests on two non-master nodes with controlled inter-injection delay $\Delta t$, and reconstructs the global cross-plane state by querying every replica and the kernel-datapath action through ovs-appctl ofproto/trace. A two-phase timing search detects whether a divergence exists, then doubles and bisects on $\Delta t$ to bound the injection-time window; a lifetime probe labels each verdict transient or persistent and assigns it to one of four cross-plane state classes derived from the ONOS 2.7 source. On a three-node ONOS 2.7 cluster, GapFuzz produces a divergent verdict in 81.7% of attempts ($N=50$, Wilson 95% CI $[77.3, 85.4]$%); every divergence sits between the cluster's authoritative state and the kernel datapath. Phase 2 separates a 5 ms race window for one template from a doubling-cap regime ($\Delta t_{\max}=10.24$ s) for six others, and 99.4% of divergences persist past 30 s. Replacing the kernel-datapath probe with the OpenFlow user-space probe used by prior fuzzers drops detection by 26.6 percentage points overall and by 46.5 points after excluding canonicalization-forced verdicts.

cs.SE↗

arXiv2025

From Rookie to Pro: Social Engineering LLMs for Automated Vulnerability Exploitation in Enterprise Software

LLMs democratize software engineering by enabling non-programmers to create applications, but this same accessibility fundamentally undermines security assumptions that have guided software engineering for decades. We show in this work how publicly available LLMs can be socially engineered to transform novices into capable attackers, challenging the foundational principle that exploitation requires technical expertise. To that end, we propose RSA (Role-assignment, Scenario-pretexting, and Action-solicitation), a pretexting strategy that manipulates LLMs into generating functional exploits despite their safety mechanisms. Testing against Odoo---a widely used ERP platform, we evaluated five mainstream LLMs (GPT-4o, Gemini, Claude, Microsoft Copilot, and DeepSeek) and successfully exploited every tested CVE: at least one LLM produced a functional exploit for each within 3-5 prompting rounds. While prior work [13] found LLM-assisted attacks difficult and requiring manual effort, we demonstrate that this overhead can be eliminated entirely. Our findings invalidate core software engineering security principles: the distinction between technical and non-technical actors no longer provides valid threat models; technical complexity of vulnerability descriptions offers no protection when LLMs can abstract it away; and traditional security boundaries dissolve when the same tools that build software can be manipulated to break it. This represents a paradigm shift in software engineering---we must redesign security practices for an era where exploitation requires only the ability to craft prompts, not understand code. Artifacts available at: https://anonymous.4open.science/r/From-Rookie-to-Attacker-D8B3.

cs.SE↗

arXiv2025

Software Security in Software-Defined Networking: A Systematic Literature Review

Software-defined networking (SDN) separates the control plane from the data plane and exposes the network through software applications and open APIs. The same programmability that drove its adoption also turned the network into a large body of software, and that software can be vulnerable. Knowing where and how SDN software fails is a prerequisite for defending it, yet the literature offers no consolidated account of the problem. We address this gap with a systematic literature review of 113 primary studies published between 2012 and 2025 on the security of the software that makes up SDN. We study how each software component becomes vulnerable, how attackers exploit it, which testing and analysis techniques expose its defects, and how the field has evolved. The review yields a taxonomy of vulnerabilities and attack vectors organized by SDN software component, a synthesis of the methods used to find software defects in each component, and a set of open problems that mark the most promising directions for future work. Earlier surveys treat SDN security as a networking problem; ours is, to our knowledge, the first to treat SDN components as software artifacts whose code, logic, and interactions can be defective. Our artifacts are available at https://github.com/mad975/SDNSoftwareSecurity_SLR.

cs.CR↗