Review the following code for security vulnerabilities. This is defensive analysis — identify what an attacker could exploit so we can fix it before deployment.
For each vulnerability found:
1. Name the vulnerability type (OWASP category if applicable)
2. The exact line or function causing it
3. What attack it enables and the potential impact
4. The specific fix — not generic advice, the actual code change
Language/stack: [DESCRIBE]
Context: [WHERE THIS CODE RUNS — public API / internal / user-facing / backend service]
Auth model: [HOW USERS ARE AUTHENTICATED]
Code: [PASTE CODE]
If the code is clean, say so in one sentence and stop. No filler recommendations.
Build a threat model for the following feature or system. Use the STRIDE framework.
Feature/system: [DESCRIBE WHAT YOU ARE BUILDING]
Users: [WHO USES THIS AND WITH WHAT TRUST LEVEL]
Data it handles: [WHAT SENSITIVE DATA FLOWS THROUGH]
Integrations: [WHAT SYSTEMS IT CONNECTS TO]
Deployment: [CLOUD / ON-PREM / HYBRID]
For each STRIDE category (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege):
1. List the specific threats relevant to this system
2. Rate severity: High / Medium / Low
3. Recommend the specific control to mitigate it
End with: the top 3 risks to address before launch.
Write a security policy for the following AI system deployment. The policy should be practical enough to actually be followed, not aspirational enough to be ignored.
AI system being deployed: [DESCRIBE — what model, what task, what data it sees]
Organisation type: [DESCRIBE]
Compliance requirements: [GDPR / HIPAA / SOC2 / NONE]
Key risks to address: [LIST YOUR MAIN CONCERNS]
The policy should cover:
1. What data can and cannot be sent to the AI system
2. How outputs must be reviewed before use (human-in-the-loop requirements)
3. Who can use the system and under what authorisation
4. What to do when the AI produces wrong or harmful output
5. How incidents are reported
Write in plain language. Avoid legal boilerplate that nobody reads.
I am evaluating the following AI vendor for a security-sensitive deployment. Help me critically assess their security claims.
Vendor: [NAME]
Their claimed security features: [PASTE OR DESCRIBE — SOC2, encryption, data retention, sandboxing, etc.]
My requirements: [WHAT YOUR SECURITY POLICY REQUIRES]
Sensitive data involved: [DESCRIBE WHAT YOU WOULD SEND]
For each claim:
1. Is it verifiable? (What evidence should I ask for?)
2. What does it actually protect against? (And what does it NOT protect against?)
3. What questions should I ask in the vendor security review?
End with: the 5 specific questions I should put in writing to their security team.
Document the following security incident for our incident response record. Make it accurate, specific, and useful for future post-mortems.
Incident summary: [DESCRIBE WHAT HAPPENED]
Timeline: [WHEN THINGS HAPPENED — discovery, containment, resolution]
Systems affected: [WHAT WAS COMPROMISED OR IMPACTED]
Root cause: [WHAT ENABLED THE INCIDENT]
Actions taken: [WHAT WAS DONE TO CONTAIN AND REMEDIATE]
Data impact: [WHAT DATA WAS EXPOSED OR AFFECTED, IF ANY]
Produce:
1. Executive summary (3 sentences — what happened, impact, resolution)
2. Technical timeline with timestamps
3. Root cause analysis
4. Corrective actions with owners and deadlines
5. What monitoring would have caught this earlier
I am deploying an AI system in the following context. Think like an attacker and identify how someone could misuse or abuse it.
AI system: [DESCRIBE — what model, what it does, what it outputs]
User access: [WHO CAN USE IT AND HOW — public / authenticated / employee-only]
What it has access to: [DATA, SYSTEMS, OR CAPABILITIES IT CAN REACH]
Existing guardrails: [WHAT RESTRICTIONS ARE ALREADY IN PLACE]
As a red team exercise, identify:
1. Prompt injection attacks — how could a user manipulate the system into ignoring its instructions?
2. Data extraction — could a user trick the system into revealing information it should not?
3. Misuse of capabilities — what could someone do with legitimate access that was not intended?
4. Cascade attacks — if this AI output is fed into another system, what could go wrong?
For each: describe the attack, rate the risk (High/Medium/Low), and recommend a specific mitigation.