AI Safety vs. AI Security: Demystifying the Distinction and Boundaries

• AI is increasingly used in critical domains, where its benefits coexist with risks from malicious use, unforeseen harmful failures, and systemic disruptions.Source: §1 · p. 1
• The terms AI Safety and AI Security are used inconsistently, and broad definitions can obscure the distinction between unintentional failures and intentional adversarial actions.Source: §1 · p. 1
• Safety addresses non-malicious failures such as bugs or misaligned objectives, whereas Security addresses deliberate attempts to subvert, manipulate, or exfiltrate from AI systems.The message-transmission analogy contrasts checksum-based detection of accidental corruption with cryptographic measures against intentional manipulation.Source: §1 · p. 2
• A system can be safe in normal conditions yet vulnerable to hacking, or secure against data breaches yet unsafe because of embedded biases.Source: §1 · p. 2
• Clearer boundaries are intended to support tailored research, resource allocation, policy, cross-disciplinary collaboration, and public trust.The paper describes distinct safety and security approaches, including robustness and ethical alignment versus adversarial modeling and cryptographic defenses.Source: §1 · p. 2

2 Terminological Landscape and Historical Context

• The paper situates its proposed distinction within a discourse where AI Safety and AI Security have varied and sometimes overlapping meanings.Source: §2 · p. 3

2.1 Evolution of “AI Safety” as a Broad Term

• Early AI Safety concerns focused on hypothetical AGI or superintelligence, aiming to keep powerful future systems beneficial through alignment, controllability, and prevention of catastrophic unintended consequences.Source: §2.1 · p. 3
• Some contemporary uses continued to treat AI Safety as an umbrella for harms including malicious use, while foundational work distinguished accidents from protection against malicious actors.The passage cites the 2025 International AI Safety Report as including risks from malicious use under a broad safety definition.Source: §2.1 · p. 3

2.2 The Case for Delineation in a Maturing Field

• Definitions remain inconsistent across communities, with some researchers placing security problems such as adversarial robustness within AI Safety and others distinguishing the fields.Source: §2.2 · p. 3
• The paper argues that increasingly diverse AI risks call for terminological precision and distinct tools and expertise for unintended flaws versus deliberate adversarial intent.It builds on calls for clarity by proposing a framework centered on intent.Source: §2.2 · p. 3

3 Core Definitions: Safety vs. Security

• Engineering distinguishes safety from unintentional harm and security from intentional harm, while AI learning, autonomy, and emergent behavior add nuance to applying these concepts.Source: §3 · pp. 3–4

3.1 AI Safety

• AI Safety is the property of avoiding unintended harmful outcomes to people, environments, or institutions despite uncertainty in inputs, goals, training data, or deployment conditions.Source: §3.1 · p. 4
• AI Safety addresses reliable operation in line with human intent and values amid uncertainty, complexity, or incomplete specification, including distribution shifts, emergent behavior, alignment, oversight, and long-term risks.The passage also identifies safe exploration and control as concerns.Source: §3.1 · p. 4

3.2 AI Security

• AI Security is the property of remaining resilient to intentional attacks on data, algorithms, or operations while preserving confidentiality, integrity, and availability.Source: §3.2 · p. 5
• AI security analysis involves valuable assets, an intentional adversary, and an exploitable vulnerability in system design, implementation, data handling, or operations.The cited examples include models, data, infrastructure, and critical functions as assets; defenses include cryptography, authentication, input sanitization, and secure development.Source: §3.2 · p. 5

4.1 The Fundamental Distinction: Intentional vs. Unintentional Threats

• AI Safety covers accidental or unintended harms, while AI Security covers malicious actors deliberately exploiting systems; the risk’s origin shapes how misuse is classified.Safety examples include malfunctions, distribution shifts, design flaws, bias, and misaligned objectives; security examples include deception, manipulation, exfiltration, and sabotage.Source: §4.1 · pp. 5–6

4.2 Interdependencies and Relationships

• Security breaches can trigger safety incidents: hijacking a vehicle can cause a crash, and prompt injection can produce harmful misinformation or instructions.Safety mechanisms may themselves be targets of adversarial subversion [39].Source: §4.2 · p. 6
• Safety flaws can become exploitable security risks, as predictable bias, weak input validation, or misaligned objectives may enable manipulation, data extraction, or unauthorized actions.The passage also connects predictable bias with targeted disinformation and identifies prompt injection as one manipulation technique.Source: §4.2 · p. 6
• Safety fixes and security defenses can interact in an adversarial cycle, while transparency and adversarial training can create cross-domain trade-offs; robustness may benefit both.A patch may introduce new safety risks or security vulnerabilities; transparency can expose vulnerabilities, and adversarial training can reduce benign accuracy or fairness.Source: §4.2 · p. 6
• Trustworthy AI requires holistic consideration of safety and security throughout the system lifecycle.Source: §4.2 · p. 6

5.1 AI Safety: Preventing Unintended Harm

• AI Safety aims to build inherently sound, beneficial, human-aligned systems that avoid unintended harm or misaligned behavior [2].Source: §5.1 · p. 7
• Safety mechanisms address alignment, natural distribution shifts, interpretability, ethical risks, fail-safes, and long-term risks, using approaches such as RLHF [38], SHAP [31], and emergency shutdowns.Other cited mechanisms include Constitutional AI [4], operational constraints, confidence thresholds, LIME [40], and safe interruptibility [2].Source: §5.1 · p. 7

5.2 AI Security: Defending Against Attacks

• AI Security protects systems from intentional attacks, including evasion and prompt injection, data poisoning, model theft, and other deliberate exploitation.Source: §5.2 · p. 7
• Security measures include watermarking against model theft [49], access controls, red teaming [20], resilience during attacks, and protection of model supply chains.Source: §5.2 · pp. 7–8

5.3 Safety-Security Synergy

• Safety and security are mutually reinforcing: interpretable models can help detect adversarial manipulation [12], while safety components such as shutdown mechanisms need cyber protection.The passage also argues that complete protection combines intrinsic safety through alignment [19] and robustness [24] with active defenses such as adversarial training and access controls.Source: §5.3 · p. 8
• Neither dimension compensates for the other, and prompt injections [22] can bypass ethical constraints; trustworthy systems integrate both from design through deployment.The building analogy contrasts a structurally sound but insecure structure with a fortified one made dangerous by faulty wiring.Source: §5.3 · pp. 8–9

• In healthcare AI, image artifacts can cause unintended X-ray misclassification, while attackers may poison training data or steal patient records.Source: §6 · p. 9
• For autonomous vehicles, safety engineering targets reliable perception in edge cases such as downpours, while security addresses GPS spoofing or remotely disabled brakes.Source: §6 · p. 9
• For LLM chatbots, safety concerns include inaccurate or biased responses, while security defenses address prompt injection used to generate malicious content or extract confidential data.Source: §6 · p. 9
• These examples distinguish an AI’s inherent capacity to avoid harm from protection against intentional malice.Source: §6 · p. 9

7 Towards Unified AI Risk Management

• Trustworthy AI requires a unified risk-management framework integrating safety and security throughout the AI lifecycle [39].The passage identifies NIST AI RMF and National AI Safety Institute guidance as resources for holistic frameworks and standards.Source: §7 · pp. 9–10
• Safety-and-security-by-design embeds both perspectives from the outset, with red teaming, adversarial training, and tamper-proof fail-safe reversion protocols.Source: §7 · p. 10
• The framework pairs technical and organizational safeguards, including robust V&V, secure MLOps, governance, incident response, accountability, audits, and real-time monitoring.Source: §7 · p. 10
• Cross-functional risk management brings together AI developers, safety engineers, cybersecurity experts, ethicists, legal professionals, and social scientists.Source: §7 · p. 10
• Security can be a precondition for safety because attacks can invalidate safety measures, while some adversarial training may improve security but reduce benign alignment [39].Source: §7 · p. 10
• The stated goal is AI that is safe by design and secure by default, supporting responsible innovation and public trust.Source: §7 · p. 10

8 Conclusion

• AI Safety aims to prevent unintentional harm and ensure systems operate as intended, while AI Security defends against intentional malicious threats.The paper characterizes these as “survival by design” and “defense by default,” respectively.Source: §8 · p. 10
• Security lapses can cause safety failures, while unaddressed safety vulnerabilities can become attack vectors for malicious misuse.Prompt injection bypassing an LLM’s safety measures is one example of a security lapse precipitating a safety failure.Source: §8 · p. 11
• Clarifying the boundaries can foster targeted research and effective governance while supporting robustly safe and demonstrably secure AI systems.The paper presents boundary clarification as a way to support responsible realization of AI’s potential.Source: §8 · p. 11