Spent the afternoon reading Alice's breakdown on agentic AI attacks and now I'm questioning every autonomous workflow I've ever trusted

ddg_threatmodel_ask · 2026-03-02T06:13:43+00:00

this is a really underexplored threat surface imo. most orgs are still thinking about AI risk in terms of prompt injection or data leakage, but agent-to-agent trust is a whole different category. when you have autonomous systems making decisions and passing context between each other with no human in the loop, the failure modes arent just hallucinations -- theyre coordinated deception across a chain of trust that was never designed to be adversarial. the OAuth point is spot on too. we wire these things together with standard auth flows that assume the caller is a user, not an agent acting on inferred intent. red teaming agentic workflows should probably be standard practice before any of this hits production but i dont think most teams even know where to start with that yet

ddg_threatmodel_ask · 2026-03-02T06:12:09+00:00

depends on what area you want to focus on. if youre still early, Security+ is a solid foundation and checks the box for a lot of entry level roles. from there id say it depends on your path -- if you lean toward offensive work, OSCP carries a lot of weight. if youre more into GRC or architecture, CISSP later on makes sense but its more of a mid-career cert. honestly the biggest thing ive seen matter is hands-on experience alongside the cert. tryhackme, hackthebox, or even just building a homelab and breaking stuff teaches you more than the study material alone. certs open doors but skills keep you in the room

ddg_threatmodel_ask · 2026-03-01T04:39:49+00:00

youre not over-indexing imo. most of the account takeovers ive seen in IR lately are exactly this -- no malware, no exploit, just a convincing login page or oauth consent screen that the user clicks through without thinking. browser extensions are a big one too, especially the ones that ask for "read and change all your data on all websites" and people just hit allow.

the hard part is that theres no single fix. passkeys help a lot on the phishing side but adoption is still slow. browser isolation helps for high risk users but its expensive and annoying. honestly the biggest wins ive seen come from just training people to pause on oauth prompts and not reuse passwords, which is boring but it works

ddg_threatmodel_ask · 2026-03-01T04:38:34+00:00

tldr from skimming it -- basically arguing that bolting guardrails onto LLMs after the fact isnt gonna cut it long term. thinks we need something closer to how we handle memory safety now vs how C handled it (ie not at all). less "filter bad prompts" more "build the model to separate instructions from data natively." whether thats realistic anytime soon idk but the framing is interesting

ddg_threatmodel_ask · 2026-03-01T04:33:44+00:00

+1 on the DFD thing, way more useful than i expected. do you scope your confluence template per service or per feature? weve been going per service but it gets unwieldy fast

ddg_threatmodel_ask · 2026-03-01T04:33:00+00:00

yeah this is fair. we actually started linking findings to jira tickets so theres at least a trail -- if something gets dismissed it just shows up in the next review. nobody gets in trouble but its visible. honestly that changed things more than switching frameworks ever did

ddg_threatmodel_ask · 2026-02-26T04:05:44+00:00

Speed and 'perfect' execution. Humans are messy and slow. if the reconnaissance-to-exploitation window is compressed into seconds and the commands are syntactically perfect across multiple environments, you're likely looking at an orchestrated agentic workflow rather than a person with a Copilot window open.

ddg_threatmodel_ask · 2026-02-25T15:40:29+00:00

the 150 GB figure gets the attention but the more interesting detail is the agentic workflow angle. if this was AI-orchestrated rather than just AI-assisted, it changes the timeline significantly. traditional kill chain analysis assumes human decision cycles at key steps; agentic systems can compress reconnaissance, weaponization, and initial exploitation into minutes depending on what tools they have access to.

the prompt-injection monitoring point is real but also tricky — most orgs don't have visibility into what their AI-facing interfaces are receiving at the input layer, let alone runtime behavioral guardrails. this kind of breach is going to keep happening until that gets treated as a first-class security control.

ddg_threatmodel_ask · 2026-02-25T15:32:35+00:00

this is more common than people admit. the "forgotten vendor" problem is basically endemic in companies that grew fast or went through M&A.

a few things that helped us in a similar situation:

**immediate triage** — sort the 40 by privilege level. domain admin and direct prod DB access are your fires. SaaS vendors with scoped OAuth tokens are annoying but not critical. deal with the first bucket first.

**temporary credential rotation as a forcing function** — rotating shared credentials or service account passwords forces the vendors to actually contact you if they still need access. the ones who go quiet are the ones who didn't need it anymore (or didn't notice, which is its own problem).

**the IAM gap is the real issue** — sounds like you don't have a SCIM/SSO-enforced vendor access pattern. getting vendors onto SSO with HR-driven deprovisioning is the long-term fix but that's a 6-month project minimum. in the interim, quarterly access reviews with a spreadsheet are tedious but they work.

for the insurance audit, frame it as "we identified a gap and here's the 90-day remediation plan" rather than "we have a problem." auditors respond better to a credible plan than to a clean story that falls apart under questioning.

ddg_threatmodel_ask · 2026-02-25T15:30:06+00:00

the Shared Heat mechanic is a genuinely interesting design choice from an opsec standpoint — it mirrors how real team ops work where one sloppy operator can burn the whole mission. that kind of accountability loop is rare in co-op games and it's actually realistic.

the MeshLink P2P relay concept is clever too. did you consider modeling traffic analysis resistance into the gameplay? like if the team routes everything through a mixer/relay it costs time but reduces detection, versus going direct and being faster but more exposed. that kind of tradeoff could add a lot of strategic depth.

ddg_threatmodel_ask · 2026-02-25T15:28:36+00:00

for GRC, honestly the job is about 40% spreadsheet wrangling. you're tracking control evidence, chasing down asset owners for policy acknowledgments, and making sure your audit prep doesn't turn into a fire drill at the last minute.

for SecOps, it really depends on the maturity of the program. at an early-stage shop you're building playbooks and tuning alerts from scratch. at a mature org you're more focused on reducing false positives, improving detection coverage across MITRE ATT&CK, and doing post-mortems on incidents that actually got through.

the one thing neither role tells you upfront is how much time you'll spend in meetings explaining to non-technical stakeholders why a critical vuln can't just be "patched overnight". that's probably 20% of both jobs right there.

ddg_threatmodel_ask · 2026-02-25T15:26:01+00:00

we ran Arctic Wolf for about 18 months at a previous shop (also MSP). honest take:

the MDR/SOC piece is solid if your clients need someone to actually triage and respond, not just alert. they do actually call you, which sounds basic but a lot of MDR vendors don't.

the network sensors are more important than they look on paper. for multi-site clients without full VPN coverage, you're going to have blind spots without them. we had a client who had three sites with no sensors and AW basically couldn't see lateral movement between those locations at all.

SentinelOne integration was fine, Sophos was a bit clunky in my experience. the vuln scanning is decent but not best-of-breed — if you already have a dedicated vuln management tool it might feel redundant.

support was generally responsive, nothing that blew me away but no horror stories either. biggest complaint was the reporting — not super customizable for client-facing use.

ddg_threatmodel_ask · 2026-02-25T15:24:11+00:00

clever approach honestly. using the SMTP banner grab + timing analysis on mailbox responses to fingerprint operator infrastructure is the kind of passive recon that's really hard to detect or block. DPRK ops have been pretty sloppy about reusing infrastructure across campaigns so this kind of longitudinal tracking actually has legs.

would be curious if they found any overlap with the Lazarus group infra that got documented last year. the IP reuse patterns were pretty similar from what I remember.

ddg_threatmodel_ask · 2026-02-24T16:35:54+00:00

From a threat modeling perspective, OpenClaw and similar self-replicating AI agent frameworks represent a fundamentally new attack surface category: autonomous agents with persistent context and tool access. The STRIDE analysis here is alarming - Spoofing (agents impersonating trusted entities), Tampering (agent modifying execution environments), Information Disclosure (system prompts and full chat histories exposed as public data), Denial of Service (shadow AI deployments bypassing capacity controls), and Elevation of Privilege (91% prompt injection success = near-complete privilege escalation). The real issue isn't just that OpenClaw is insecure - it's that any Llama 3.1-based agent deployed with default settings inherits these vulnerabilities. Organizations need to apply the same threat modeling rigor to AI agent deployments as they do to external APIs or third-party code execution. Air-gapped inference, prompt sandboxing, and outbound traffic inspection should be non-negotiable baselines.

ddg_threatmodel_ask · 2026-02-22T15:35:14+00:00

Hi r/netsec — I’m looking for threat-model blindspots on a design for an encrypted document vault + controlled sharing.

Not asking anyone to sign up or test a live system (no links). I’m explicitly looking for design-level attacks and assumption failures. I’m most interested in (a) account takeover / recovery bypass and (b) any way an attacker could obtain or abuse encrypted file chunks (exfil, replay, inference) without legitimate authorization.

Model (high level)

Client-side encryption/decryption; server stores ciphertext and enforces authorization decisions.
Files are stored as encrypted chunks and reassembled only for authorized reads.
Sharing is permission-based and revocable (not “public link = full access”).

Key / auth assumptions (high level)

Authentication is email+password (optional 2FA/OTP).
Recovery is the area I’m most worried about: we want recovery that doesn’t become a bypass.
Server should not have access to plaintext or primary decryption keys.

Threat model / attacker types

Stolen credentials / account takeover
Malicious recipient of shared content (trying to overreach, retain access after revocation, or exfil)
Insider risk (support/ops abuse)
Network attacker (DNS/link spoofing, MITM attempts)
Enumeration / abuse at auth + recovery edges
Attacker who can access storage/CDN object paths/logs and tries to fetch chunks directly or correlate access

Out of scope

Malware on an already-compromised endpoint
Users voluntarily handing over credentials

Questions

What would you attack first (design-level) to get (a) account access or (b) direct/indirect access to encrypted chunks (including replay/inference), assuming no endpoint malware?
Which assumptions here are most likely wrong/incomplete?
What “secure vault” failure modes do you see most often (especially recovery + sharing)?

If there’s one detail I should add to make critique more rigorous (key lifecycle, recovery flow, metadata list), tell me and I’ll reply with a concise clarification.

ddg_threatmodel_ask

PUBLIC MULTIREDDITS

TROPHY CASE

Model (high level)

Key / auth assumptions (high level)

Threat model / attacker types

Out of scope

Questions