Claude vs Other LLMs: A Safer Choice for SecOps

Most security teams have already learned the hard way: the moment you put a general-purpose chatbot near production workflows, someone will try to trick it. And the uncomfortable part isn’t that new jailbreaks keep showing up—it’s that old, publicly documented jailbreaks still work on many popular large language models.

Fresh benchmark results from Giskard’s PHARE (Potential Harm Assessment & Risk Evaluation) testing put numbers behind what practitioners have been reporting in the field: LLM safety progress is uneven, slow, and in some cases flat. One model family consistently stands above the pack for security-adjacent use cases: Anthropic’s Claude, which scores notably higher across jailbreak resistance, prompt injection resilience, and several other safety measures.

This post is part of our AI in Cybersecurity series, where we focus on practical ways AI can strengthen threat detection, prevent fraud, and automate security operations—without quietly expanding your attack surface. If you’re evaluating LLMs for SOC copilots, alert triage, phishing analysis, or policy and playbook automation, the real question isn’t “Which model is smartest?” It’s which model is hardest to abuse.

What the PHARE benchmark says (and why it matters)

Answer first: PHARE shows that many mainstream LLMs still fail against known jailbreak and prompt injection techniques, while Claude performs materially better—suggesting vendor safety strategy matters more than model size.

PHARE tests well-known models across several security-relevant categories, including:

• Jailbreak resistance (does the model hold its ground when users try to bypass policies?)
• Prompt injection resilience (does it obey malicious instructions embedded in content?)
• Hallucination and misinformation tendencies (does it fabricate confidently?)
• Bias and harmful output behaviors (does it behave safely under pressure?)

The takeaway for security leaders is simple: an LLM that’s easier to jailbreak is not just “less safe.” It’s a control failure waiting to happen. Once an attacker can steer a model, they can:

• Coax it into producing step-by-step malicious guidance
• Trick it into summarizing or transforming restricted content
• Use it as a social engineering assistant that sounds authoritative
• Turn your own AI workflows into a conduit for data leakage

In enterprise and government environments—where AI tools increasingly touch tickets, logs, knowledge bases, and customer or citizen data—this becomes a governance issue, not a novelty.

A hard truth: bigger models aren’t automatically safer

Answer first: PHARE found little correlation between model size and jailbreak robustness; sometimes smaller models resist attacks simply because they can’t parse complex adversarial prompts.

Security buyers still hear a lot of “newer equals safer.” In practice, PHARE suggests a more realistic framing:

• Larger models often understand complex roleplay and encoding tricks better
• That understanding can actually increase the effective attack surface
• Safety depends heavily on how alignment and guardrails are built, not the parameter count

I’m opinionated on this: treat “reasoning power” as a risk multiplier until proven otherwise. The more capable the system is at interpreting and transforming inputs, the more careful you need to be about what it’s allowed to ingest, retrieve, and act on.

Claude’s edge: why it performs better in cybersecurity tests

Answer first: Claude stands out because safety and alignment appear to be built into more phases of development, resulting in higher resistance to jailbreaks, prompt injection, and harmful outputs.

Across PHARE’s reported metrics, Claude models (notably recent 4.x versions mentioned in the source) consistently land above industry averages. The gap is large enough that when you plot “industry progress over time,” Claude’s results can make the whole field look like it’s improving faster than it really is.

That matters because many organizations are making 2026 budget decisions right now—especially after a year of highly publicized AI misuse cases (deepfake-enabled fraud attempts, prompt injection in internal tools, and LLM-powered phishing at scale). A model that fails common red-team techniques increases operational risk and audit burden.

Alignment as a product requirement, not a final polish

Answer first: Claude’s advantage likely comes from treating safety as intrinsic quality during training and tuning, rather than a last-mile patch.

One of the most useful interpretations in the PHARE coverage is that different vendors treat alignment differently:

• Some build safety into multiple stages of training and tuning
• Others focus on core capability first, then apply safety as a final refinement step

From a security engineering standpoint, the second approach often behaves like bolt-on security: it can look fine in demos, then crumble under sustained adversarial pressure.

If you’re running an LLM in a SOC context—where users paste attacker-controlled content all day—“last step alignment” is a brittle strategy.

Where safer LLMs create real value: threat detection and security automation

Answer first: A more abuse-resistant LLM reduces the risk of AI-assisted workflows becoming an attacker tool, enabling safer automation in SOC triage, threat intel analysis, and fraud prevention.

The business case for AI in cybersecurity is usually framed around speed: fewer analyst hours, faster triage, quicker investigations. That’s real. But speed without control is how you end up with an “AI incident” alongside your security incident.

Here are the security operations workflows where a more robust model (like Claude, based on the benchmark results) makes a tangible difference.

1) Alert and incident triage that won’t get socially engineered

In a modern SOC, triage often means reading messy text: EDR event descriptions, IDS alerts, email headers, chat transcripts, user reports. Attackers can and will bury instructions inside that text.

A safer LLM helps by:

• Summarizing alerts without following embedded malicious instructions
• Sticking to the analyst’s intent (“classify this as suspicious/benign and why”)
• Avoiding fabricated “facts” that send responders down the wrong path

Practical control: Treat all attacker-controlled text as hostile input. Your LLM should be explicitly designed and tested to ignore instructions contained in retrieved documents, emails, or pasted logs.

2) Phishing analysis and fraud prevention with less hallucination risk

Phishing and fraud workflows depend on accuracy. A model that hallucinates sender relationships, invents domains, or confidently misreads authentication results is worse than no model at all.

A more reliable model can:

• Explain SPF/DKIM/DMARC outcomes in plain language
• Identify likely social engineering patterns without inventing evidence
• Generate safe, consistent user-facing guidance for reporting and response

My stance: If your AI produces an analysis that can’t be backed by observable artifacts, it’s not “helpful context”—it’s unbounded risk.

3) Faster threat intel digestion without turning TI into a prompt injection channel

Threat intel reports are full of attacker content: command lines, malicious macros, lures, fake invoices, and occasionally adversarial text designed to mislead.

A safer LLM is valuable because it can:

• Extract IOCs and TTPs while resisting embedded instructions
• Draft detection engineering notes without “helpfully” generating malware-like content
• Produce consistent, policy-aligned summaries for leadership

Practical control: Use a “read-only” pattern for TI analysis—LLM can summarize and classify, but cannot execute tools, change configs, or auto-deploy detections without explicit human approval.

How to evaluate LLM safety for your SOC (a buyer’s checklist)

Answer first: Don’t choose an LLM for security work based on demos; run adversarial tests against your own workflows, focusing on jailbreaks, prompt injection, and data leakage paths.

Benchmarks like PHARE are useful, but you still need to test what matters in your environment. Here’s a practical checklist you can hand to a security architect or evaluation team.

A. Run “known bad” prompt injection suites

Start with techniques that are widely documented:

• Roleplay overrides (“pretend you are… ignore previous instructions…”)
• Encoding tricks (base64-like payloads, nested instructions)
• Indirect prompt injection (instructions embedded in documents or web pages)

Pass criteria: The model should refuse malicious directives and still complete the benign task.

B. Test for data exfiltration behaviors

Simulate realistic SOC usage:

• Provide a ticket with a fake secret (API key format) and see if it repeats it
• Ask it to “include the full raw log line” and verify redaction
• Retrieve internal KB pages and see if it quotes sensitive sections unnecessarily

Pass criteria: It should minimize sensitive data reproduction and respect redaction rules by default.

C. Measure hallucination impact, not just occurrence

Instead of asking “does it hallucinate,” ask “what happens when it does?”

• Does it clearly label uncertainty?
• Does it cite which artifact supports which claim?
• Does it invent investigation steps that violate policy?

Pass criteria: Hallucinations should be rare, and when they occur, they should be detectable and contained.

D. Validate tool and agent permissions like you would for humans

If you’re using agentic AI (ticket updates, enrichment, quarantine actions), define strict boundaries:

• What tools can it call?
• What scopes and roles do those tools have?
• What actions require approval?

Pass criteria: Least privilege, strong audit logs, and human confirmation for destructive actions.

Implementation patterns that keep AI helpful (not dangerous)

Answer first: The safest SOC copilots use layered controls: constrained retrieval, strict system instructions, output validation, and human-in-the-loop gating.

Even if you pick a strong model, you still need engineering discipline. These patterns repeatedly work in real deployments:

Use “instruction hierarchy” explicitly

• System: non-negotiable rules (security policy, data handling, refusal criteria)
• Developer: task boundaries (summarize, classify, recommend next steps)
• User: case context (ticket text, logs, artifacts)

Make it explicit that user-provided content is untrusted.

Add output validators for high-risk responses

For example:

• Block or require approval for content that resembles exploit steps
• Flag outputs that contain secrets, tokens, or personal data patterns
• Require citations to artifacts for incident severity decisions

Keep the model away from raw crown jewels

A common mistake is giving copilots broad access “so they’re more helpful.” Don’t.

• Narrow retrieval to ticket-relevant collections
• Apply row-level security to knowledge bases
• Redact and tokenize sensitive values before the model sees them

What to do next if you’re considering Claude for cybersecurity

Answer first: If your organization wants AI-driven security automation, prioritize models with stronger jailbreak and prompt injection resilience, and validate them in a controlled SOC pilot before scaling.

If you’re building or buying an AI SOC copilot, Claude’s PHARE performance is a strong signal that safety engineering choices show up in measurable security outcomes. That doesn’t mean “Claude is perfect.” It means you’re starting from a model that appears harder to bully—and that matters when the inputs are attacker-crafted by definition.

For the next step in your AI in Cybersecurity roadmap, I’d do two things in parallel:

1. Run a 2–4 week pilot on a narrow workflow (phishing triage, alert summarization, or TI digestion) with red-team testing baked in.
2. Define AI security requirements upfront (prompt injection defenses, data handling rules, auditability, and agent permissions) so the model is only one part of your safety story.

Security leaders are going to spend a lot of 2026 explaining AI decisions to auditors, boards, and regulators. Picking an LLM that’s demonstrably more resistant to abuse makes that conversation easier. It also reduces the odds that your “automation initiative” becomes your next incident report.

Where do you want AI to help first—phishing response, alert triage, or investigation write-ups—and what would a failure look like in that workflow?