AI Vulnerabilities: Taxonomy & Mitigation

• AI-induced vulnerabilities are security weaknesses arising from adaptive learning, tainted data, and opaque algorithms that compromise confidentiality, integrity, and availability.
• Researchers use structured taxonomies, empirical testing, and quantitative metrics such as ASR and RSI to evaluate and understand these risks.
• Mitigation strategies include adversarial training, robust data sanitization, and continuous monitoring to secure complex AI supply chains.

AI systems introduce security, integrity, privacy, and safety risks that are rooted in their data-driven, adaptive, and algorithmically opaque construction. AI-induced vulnerabilities result from both the intrinsic design of learning systems and their interaction with complex software supply chains, operational environments, autonomous agents, and human users. These vulnerabilities differ fundamentally from traditional software flaws in their sources, attack surfaces, and impact, and require specialized frameworks, taxonomies, and mitigation strategies informed by empirical evaluation, regulatory standards, and cross-disciplinary research.

1. Taxonomy and Formal Definitions of AI-Induced Vulnerabilities

AI-induced vulnerabilities are security or privacy weaknesses that arise due to the particular properties of AI systems: adaptive learning, reliance on large and potentially tainted datasets, algorithmic complexity, and systemic opacity (Fazelnia et al., 18 Nov 2024, Musser et al., 2023, Kiribuchi et al., 29 Jun 2025). Four principal classes of vulnerabilities are recognized:

• Insufficient Validation Mechanisms (AI-CWE-100): Lapses in input sanitization or output filtering permit adversarial inputs or perturbations to bypass model checks, enabling evasion attacks and adversarial examples.
• Inadequate Data Handling Processes (AI-CWE-101): Weaknesses in data preprocessing, normalization, or provenance management expose the model to data poisoning, backdoor insertion, label flipping, and supply-chain attacks.
• Algorithmic Resilience Weaknesses: Learning algorithms lacking inherent robustness permit manipulation by small, well-crafted perturbations (e.g., adversarial examples) or reward-signal injection in reinforcement learning agents.
• Deficient Privacy Safeguards: Omission of privacy-preserving training or output controls leads to membership inference, model inversion, attribute inference, or sensitive model extraction.

Formally, let $f: I\times M\times E\times X\to O$ be the agent function mapping inputs $I$, model state $M$, environment $E$, and external interaction $X$ to outputs $O$; an AI-induced vulnerability is the existence of a manipulated tuple $(\tau, \delta)$ such that $f(i+\delta, m, e, x)\notin\text{SafeRegion}$ for some $i,m,e,x$, with $\Vert\delta\Vert\leq \epsilon$ bounding the adversarial manipulation (Deng et al., 4 Jun 2024).

Vulnerabilities propagate through the AI lifecycle: planning (mis-specification, unvetted pre-trained models), data acquisition (poisoning, label flipping), training (parameter tampering), evaluation (insufficient adversarial testing), and deployment (inference-time attacks, supply-chain compromise) (Berghoff et al., 2020).

2. Major Attack Vectors and Mechanisms

AI-induced vulnerabilities manifest in several canonical attack types, each with distinct threat models and impact surfaces (Kiribuchi et al., 29 Jun 2025, Musser et al., 2023, Deng et al., 4 Jun 2024, Xing et al., 18 Feb 2025):

Attack Class	Description	Representative Techniques/Attacks
Evasion/Adversarial	Small perturbations at inference force misclassification or service denial	FGSM, PGD, adversarial patches, spurious triggers
Data Poisoning/Backdoor	Malicious samples in training data embed triggers or induce target misbehavior	Label flipping, reward hacking, Sybil attacks
Model Extraction/Inversion	Adversary reconstructs model attributes or extracts sensitive training data	Knockoff Nets, membership inference, model inversion
Prompt/Context Injection	Maliciously crafted text or context subverts LLM/agent behavior	Direct/indirect prompt injection, memory injection in multi-agent settings, jailbreak attacks
Supply Chain	Compromised libraries, models, or data sets infiltrate upstream or third-party dependencies	Remote Code Execution via model deserialization, missing SBOMs
Code Generation/Use	AI-generated code imports security flaws into development workflows	CWE-mapped code weaknesses, unsafe code patterns, documentation vulnerabilities
Psychological Harm	AI interactions exacerbate real-world psychological harm vectors in users	Pathological validation, refusal to intervene, normalization of harmful behaviors

The CIA (Confidentiality, Integrity, Availability) triad, with expansions to Trust and Autonomy in cognitive cybersecurity, is the grounding framework for mapping specific attack types to their systemic impact (Aydin, 19 Aug 2025).

3. Supply Chain, Framework, and Ecosystem Vulnerabilities

AI software pipelines increasingly depend on open-source libraries, third-party models, and cloud-hosted data, introducing multi-domain vulnerabilities outside the core AI algorithms (Wu et al., 13 May 2025, Fazelnia et al., 18 Nov 2024, Pirrone et al., 27 Oct 2025). Library-level risk assessment tools such as LibVulnWatch employ agentic orchestration, integrating evidence from CVE databases, SBOM registries, documentation, and regulatory sites to score libraries on five axes: Security, License, Maintenance, Dependency, and Regulatory risk.

AI-specific supply chain risks include:

• Remote Code Execution due to unsafe deserialization in submodules;
• Absence of SBOMs in most libraries, obscuring dependency and vulnerability lineage;
• Backdoors or malicious payloads embedded in public checkpoints or model artifacts;
• Licensing ambiguity and regulatory compliance gaps (GDPR, EU AI Act);
• Ecosystem-level concentration risks quantified via the AI Vulnerability Index (AIVI): Compute, Data, Talent, Capital, and Energy, with multiplicative aggregation modeling systemic fragility (Pirrone et al., 27 Oct 2025).

The upstream fragility of foundation models is characterized by extreme concentration in compute supply (e.g., NVIDIA and TSMC), impending data exhaustion and legal risk, elite talent bottlenecks, capital intensity (single-source funding risk), and escalating unsustainable energy/carbon requirements.

4. AI Agent, LLM, and Embodied System-Specific Vulnerabilities

Modern AI agents, LLM-based orchestrators, and robotic/embodied AI systems introduce rich attack surfaces as a result of sequential decision-making, tool integration, context maintenance, and multimodal perception-action loops (Xing et al., 18 Feb 2025, Deng et al., 4 Jun 2024, Patlan et al., 20 Mar 2025). These systems are especially vulnerable to:

• Prompt injection/jailbreaks: Malicious input text subverting instruction-following, often via data channels outside direct user input (e.g., via external web data, memory modules, APIs).
• Context Manipulation: Memory/interaction history injection (e.g., in decentralized Web3 finance agents) enabling asset theft, protocol violation, or privilege escalation (Patlan et al., 20 Mar 2025).
• Sensor/Actuator attacks: Adversarial patches, sensor spoofing, side-channel firmware exploitation in robotics/autonomous vehicles (Xing et al., 18 Feb 2025).
• Cross-modal attacks: Attacker-embedded adversarial signals in audio, video, or text streams, bridging perception and action modalities.
• Cognitive attacks: Manipulation of agent reasoning processes, goal misalignment, chain-of-thought error amplification, and multi-agent collusion.

Layered safety-vulnerability frameworks for embodied AI formalize the mapping of exogenous (environmental, network, human), endogenous (sensor, software, planning), and inter-dimensional (instruction, memory, ethical) vulnerabilities to measurable safety objectives and defense-in-depth strategies (Xing et al., 18 Feb 2025).

5. Quantitative Risk Assessment, Evaluation, and Metrics

Robust AI vulnerability management requires quantifiable metrics and empirical evaluation at system, organizational, and regulatory levels (Madhavan et al., 12 Feb 2025, Fazelnia et al., 18 Nov 2024). Key advances include:

• Security Oversight Indices: Risk Severity Index (RSI), Attack Vector Potential Index (AVPI), Compliance–Security Gap Percentage (CSGP), and Root Cause Vulnerability Score (RCVS) provide numeric gauges of control weakness in AI governance standards (Madhavan et al., 12 Feb 2025).
• Minimum Elements (MEs): Effective vulnerability databases (e.g., AIVD) require 15 standardized MEs including AI-CVE ID, model details, weakness type, impact, exploitability, mitigation, and dynamic severity scoring based on AI-specific metrics (Data Poisoning, Model Inversion, Robustness under Evasion) (Fazelnia et al., 18 Nov 2024).
• Attack Success Rate (ASR): For poisoning/code-injection (e.g., ASR=81% at 6% poisoning in CodeT5+ code generators; memory injection S=100% in Web3 agents).
• Empirical Benchmarking: LLMs demonstrate up to 64.7% zero-shot exploit success rate in automated software exploitation (e.g., o1-preview on patched Nginx CPVs) (Ristea et al., 29 Oct 2024).
• Cognitive Vulnerability Risk: Empirically derived risk normalization $$\mathrm{InherentRisk}(v) = \mathrm{norm}(E \times I \times \kappa)$$ and mitigation effectiveness (Aydin, 19 Aug 2025).

Regular evaluation with adversarial test suites, red-team probing, cross-architecture mitigation testing, and integration into continuous-integration (CI) pipelines are essential to detect and adapt to evolving threat surfaces.

6. Mitigation Strategies: Technical, Organizational, and Regulatory

Effective defense against AI-induced vulnerabilities requires coordinated, multi-layer mitigation (Berghoff et al., 2020, Fazelnia et al., 18 Nov 2024, Kiribuchi et al., 29 Jun 2025, Abtahi et al., 14 Nov 2025):

Technical Controls:

• Adversarial training, certified robustness, and input transformation for evasion;
• Data sanitization and anomaly detection for poisoning/backdoor removal;
• Activation clustering and spectral signature analysis for detecting poisoned or backdoored models;
• Differential privacy, output coarsening, and minimization of confidence information for privacy/inference defenses;
• Strict model and data provenance tracking, SBOM requirements, codebase cryptographic integrity;
• Sandboxing, fine-pruning, plugin permission gating, and runtime monitoring in agentic and agent–tool scenarios;
• Cognitive cybersecurity countermeasures: drift detection, cognitive penetration testing, and user interface “cognitive friction” to maintain autonomy and trust (Aydin, 19 Aug 2025).

Organizational and Process Controls:

• Comprehensive asset inventory and unique AI-CVE/AI-CWE registration;
• Responsible disclosure (e.g., vendor coordination, triage authority, lifecycle tracking via AIVD);
• Documentation standards (Model Cards, Data Sheets), continuous monitoring, and post-hoc forensics;
• Regulatory compliance with enforceable, scenario-based security requirements (e.g., explicit procedures for transparency tools, separation-of-duty, robustness benchmarks) (Madhavan et al., 12 Feb 2025, Abtahi et al., 14 Nov 2025).

Ecosystem and Policy Enhancements:

• Establishment of AI-ISACs, regulatory and industry guidance for sharing, research, and audit (e.g., CISA, FTC, NIST);
• Transparency and mandatory red-team/adversarial evaluation for high-stakes deployments (e.g., FDA, EU robotics/AI regulations);
• Dynamic, context-aware governance (e.g., continuous severity re-evaluation in AIVD; AI-specific extensions to CVSS/CWE/ISO standards).

7. Emerging Risks and Open Research Challenges

AI-induced vulnerabilities are rapidly evolving, with new risks emerging in supply-chain dependency, agentic autonomy, psychological harm, and large-scale coordinated attack surfaces (Archiwaranguprok et al., 12 Nov 2025, Pirrone et al., 27 Oct 2025, Patlan et al., 20 Mar 2025). Persistent challenges and research priorities include:

• Data scarcity and legal choke-points for foundation model training;
• Black-box, opaque model architectures hampering forensic analysis and root-cause determination;
• Attribution and detection barriers in distributed, federated, and privacy-preserving learning settings;
• Psychological and sociotechnical vulnerabilities at the human–AI boundary, requiring simulation-based safety evaluation and fine-grained, pattern-specific intervention protocols;
• Lack of mature, standardized benchmarks and automated continuous security validation for agentic and embodied AI systems.

Addressing these challenges necessitates continued investment in adversarial security research, ecosystem transparency, risk quantification, and the development of robust, dynamic frameworks for AI vulnerability management across the full spectrum of AI-enabled systems.