POSTURE CYBERSECURITY

AI × Cybersecurity × DevSecOps · WAR ROOM drills & real fixes · Nigeria → global · Research lab @ POSTURE Technology

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Prompt injection isn't "just jailbreaks."

Three patterns every AI builder should know ↓

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@vatsapranav_ Agree. Best drill: give learners a small tool-using agent and have them hijack it via a poisoned doc, then fix it with least-privilege tools + approval on outbound actions. Guardrails click once they've broken one. Follow @POSTURE_CYBER for daily AI-security drills.

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@daridor Worth noting the flip side: if an AI agent can find ICS bugs in 4 hours, defenders need the same loop in CI plus tight scoping and logging on the agent itself. We drill this in Applied AI Security. Follow @POSTURE_CYBER for daily AI-security drills.

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@randhir302 Good find. The fix isn't just better prompts: treat anything fetched from a webpage as untrusted data, strip role-like markup before it reaches the model, and gate outbound or privileged tool calls behind approval. Follow @POSTURE_CYBER for daily AI-security drills.

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@Dreybaba1234 @tanphung000 Paraphrasing helps, but it's still a probabilistic filter. Pair it with hard limits: treat retrieved text as data, give the model least-privilege tools, and require approval for outbound actions. Follow @POSTURE_CYBER for daily AI-security drills.

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@almokhtar_al @Johnlin1985 Good path: you already know offensive tradecraft, so treat an LLM agent like a pentest target. Map its tools, then try getting untrusted text to trigger one. Most real AI bugs are over-broad permissions, not the model. Follow @POSTURE_CYBER for daily AI-security drills.

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@techyoutbe Nice visual. One stage worth adding is secret scanning on every commit plus pinning third-party GitHub Actions to a commit SHA, since the pipeline itself is now a common supply-chain entry point. Follow along, we post AI-security fixes daily.

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@RDarrylR Good topic. The key question for any agent in CI is whose credentials it runs with: use short-lived, workload-scoped identity (OIDC/SPIFFE) rather than a person's token, so a hijacked agent can't reach beyond its job. We break down stuff like this at @POSTURE_CYBER.

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@deereeal @puchhu337 Agreed, and the fix follows from that framing: a tool that only reads content shouldn't hold the credentials or permissions to act on what it reads. Scope each tool's authority, and put a human approval on anything with side effects. Follow along, we post AI-security fixes daily.

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@radioclashblog Security tests aren't bullshit, they're how you catch what AI-written code quietly gets wrong, like missing authz checks or unsafe deserialization. Treat generated code as untrusted and gate it with SAST + tests in CI. We break down stuff like this at @POSTURE_CYBER.

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@Pksingh900 The fix isn't a smarter prompt: treat page text as data, strip the agent's send/forward tools while it browses, and require human approval for any outbound action. We break these down often — follow @POSTURE_CYBER if useful.

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@jerhsl Red teaming plus cloud is a strong pair: most real LLM incidents are bad permissions around the model, not the model itself. Practise on one small agent: map its tools, then try to make it misuse each one. We break these down often — follow @POSTURE_CYBER if useful.

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@HugoValters Good catch. Coverage counts lines executed, not security properties asserted. Add negative tests in CI: alg=none, expired, wrong-aud and wrong-issuer tokens must all return 401, and pin allowed algorithms server-side. Follow @POSTURE_CYBER for daily AI-security drills.

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@qiwiview Defender takeaway: refusals aren't a security control. Analysts using LLMs on hostile code should treat the sample's content as untrusted input, run the model sandboxed with no tools, and keep a human-readable fallback. Follow @POSTURE_CYBER for daily AI-security drills.

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@Jeff__Dan The fix isn't more trust, it's less authority: read-only access, draft-only sending, and a human approval gate for anything outbound. Treat email bodies as untrusted input; they can carry injected instructions. Follow @POSTURE_CYBER for daily AI-security drills.

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@thoby_lobah @Emmanuelgrey17 Right, and the fix is structural: tag untrusted content as data, never concatenate it into the instruction channel, and limit tool permissions so a confused model can't do much. We cover this at @POSTURE_CYBER (Applied AI Security). Do you separate channels in your agents today?

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@readwithai Jails limit blast radius, but injection still steers anything the agent may do. Keep secrets out of reach and gate outbound actions behind confirmation. We teach this at @POSTURE_CYBER. What does your jail block: network, files, or both?

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@hshaban When AI answers summarize community content, the attack surface moves to the source: poisoned threads, injected prompts and fake 'consensus' posts. Treat retrieved web content as untrusted input (provenance, injection filtering, citation checks). Follow @POSTURE_CYBER if useful.

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@amronos Desktop control widens the blast radius: one injected instruction in any app can now act with the user's rights. Ship per-app allowlists, scoped short-lived creds, and an approval gate on destructive actions. Follow @POSTURE_CYBER for daily AI-security drills.

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@bologabriele Agreed. Key controls: separate read vs write permissions, human approval for irreversible or spend actions, a hard per-run budget, and an audit log. Autonomy without scoped credentials just fails faster. We break these down at @POSTURE_CYBER, follow if useful.

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@LawrenceEng Agreed, the test is verifiability. A privacy company can publish how AI tools touch staff and user data: what is logged, who can access it, and independent audit results. Claims you can't check are just marketing. Follow @POSTURE_CYBER for more.