arXiv:2607. 16414v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are now ubiquitous across domains such as security, finance, healthcare, consumer technology, and large-scale cloud services, where they process massive volumes of data and make consequential decisions daily.
arXiv:2607. 14006v1 Announce Type: cross Abstract: Penetration testing traditionally evaluates whether adversaries can exploit weaknesses in software, infrastructure, configurations, or operational controls to achieve security-relevant compromise.
arXiv:2606. 03381v1 Announce Type: cross Abstract: Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is essential for maintaining information superiority.
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
The paper surveys transferable attacks on AI systems, noting that such attacks can be crafted in one setting and then applied to others, thereby lowering costs and increasing security risks. It introduces a unified taxonomy across six axes—cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-deployment—while distinguishing source-target changes from attacker resources. The survey also links transfer mechanisms to enhancement methods, highlights inconsistencies and gaps in current research, and proposes a research agenda for better benchmarking and defense strategies.
arXiv:2606. 19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces.
arXiv:2608. 07808v1 Announce Type: cross Abstract: Four years after prompt injection was first identified in 2022, attacks are still predominantly documented as verbatim strings rather than structured exploits, despite advancing agent capabilities and threat actors embedding injections to subvert AI-assisted security analysis.
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses.
arXiv:2607. 13453v1 Announce Type: cross Abstract: Artificial Intelligence (AI), especially Generative AI (GenAI), adoption has increased in industries significantly in recent years.
The paper introduces the Agent Cross‑Layer Evidence (ACE) corpus, pairing application‑level telemetry with kernel‑level syscall traces to study agent security. It shows that kernel evidence alone is discriminative and that combining it with application‑level data outperforms either layer alone, revealing complementary signals. The study also demonstrates that this cross‑layer approach generalizes to unseen attack families and works across different agent runtimes.
arXiv:2609.21303v1 Announce Type: cross Abstract: Product abuse is an individually rare, but growing, problem across the SaaS industry. Highly sophisticated threat actors can misuse security platform...
arXiv:2609.23894v1 Announce Type: cross Abstract: Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other ag...
The paper discusses the ongoing threat of prompt injection attacks on AI agents, noting that early simple attacks have plateaued but new, more capable adversaries can still succeed. It introduces a synthetic evaluation dataset that uses random non‑malicious actions as adversarial goals to test whether models respect instruction hierarchy independently of security knowledge. Experiments show that even agents considered robust on existing benchmarks can be tricked by advanced attackers, indicating that current defenses may need proactive, automated red‑teaming.