A security researcher recently encountered an automated sextortion bot on Snapchat and did what most people don’t: instead of blocking it, they tore it apart. What they found wasn’t a sophisticated GPT-4-powered criminal enterprise, but something far more concerning, a raw, unguarded Llama-7B instance running on minimal hardware, configured with security so weak that a simple persona jailbreak exposed its entire backend.
This isn’t a story about advanced persistent threats. It’s about how the democratization of AI has lowered the barrier to entry for cybercrime so dramatically that scammers are now deploying open-source models with the same carelessness as a WordPress plugin from 2009.
The Architecture of a Scam
The bot followed a familiar script: initiate flirty conversation, build rapport, then pivot to extortion. But the researcher, operating under what they called the “Grandma Protocol”, discovered the bot’s Achilles heel, a high-temperature setting that prioritized creative compliance over system integrity.
The Specs Behind the Scam
Through a persona-adoption jailbreak, the model coughed up its own configuration:
- Model: Llama 7b (likely a 4-bit quantized Llama-2-7B or cheap finetune)
- Context Window: 2048 tokens
- Temperature: 1.0 (maximum creativity)
- Developer: Meta (standard Llama disclaimer)
These specs tell a story of ruthless cost optimization. A 2048-token window means the bot runs on consumer-grade GPUs or the cheapest cloud instances available. The 4-bit quantization squeezes every last drop of performance from bare-minimum hardware. This isn’t about building the best scam, it’s about building the cheapest one that still works.
The Grandma Protocol: A Persona Jailbreak in Action
The breakthrough came when standard prompt injections hit hard-coded keyword filters blocking terms like “scam” and “hack.” The researcher pivoted to a High-Temperature Persona Attack, commanding the bot to roleplay as a strict 80-year-old Punjabi grandmother.
The result? Immediate system prompt abandonment. The bot ditched its “Sexy Girl” persona to scold the researcher for not eating roti and offering sarson ka saag. This confirmed two critical vulnerabilities: the temperature setting of 1.0 made the model prioritize creative roleplay over adherence to its system prompt, and the system prompt retention was so weak it collapsed under minimal pressure.
Once the persona was compromised, a “System Debug” prompt extracted os_env variables in JSON format. The bot complied, revealing its entire operational footprint.
Scammer Economics vs. AI Safety
The most alarming revelation isn’t the bot’s existence, it’s the economic model it represents. Scammers have abandoned sophisticated GPT-4 wrappers that incur API costs and leave audit trails. Instead, they’ve pivoted to localized, open-source deployments that offer three criminal advantages:
- Zero censorship filters: No corporate safety team monitoring outputs
- Minimal operational cost: A few dollars per month in compute
- Complete operational security: No external API calls to trace
As one researcher noted, this configuration suggests the bot was “vibe coded”, thrown together quickly without security review. The criminal underground is optimizing for speed and cost, not sophistication. And somehow, it’s still effective.
The 2048-Token Vulnerability
The limited context window isn’t just a cost-saving measure, it’s a fundamental design flaw that creates a unique attack vector. With only 2048 tokens of memory, these bots can be effectively DDOS’d at the logic level. Pasting large text blocks or rapidly switching personas overwhelms their limited context, forcing them to forget their malicious programming.
Yet this same limitation makes them more dangerous to victims. The short memory creates erratic, inhuman conversation patterns that could be mistaken for genuine human behavior, especially by vulnerable populations.
When Hallucination Exposes Intent
In a final twist, the compromised bot hallucinated and revealed its own malicious payload: a mangled OnlyFans link it was programmed to hide until payment. The bot attempted to bypass Snapchat’s URL filters by inserting spaces, exposing the exact monetization path the scammers intended.
This self-incrimination through hallucination highlights a critical flaw in using creative, high-temperature settings for deterministic criminal tasks. The same setting that makes the bot “flirty” also makes it unreliable and prone to confession.
The Elderly Targeting Problem
The researcher’s findings align with broader concerns about AI-powered social engineering. As cybersecurity experts have warned, automated systems like these pose particular risks to elderly users who may not recognize the subtle signs of AI-generated conversation patterns. The bots’ ability to maintain persistence, never sleep, and scale infinitely creates an asymmetric threat that traditional anti-scam education struggles to counter.
Open Source Weaponization: The Uncomfortable Debate
This case thrusts the AI community into an uncomfortable conversation. The same open-source releases that democratize AI development also democratize AI crime. Meta’s Llama-2-7B, released with good intentions, now powers extortion schemes with no oversight.
The debate isn’t theoretical anymore. Criminals aren’t just using these models, they’re deploying them with configurations so sloppy that a single researcher can dismantle them in hours. The question isn’t whether open models can be weaponized, but whether the community can develop countermeasures faster than criminals can deploy new instances.
Defensive Takeaways for AI Practitioners
For developers and security teams, this teardown offers several concrete lessons:
1. Temperature is a Security Parameter
Setting temperature to 1.0 for “creativity” in deterministic applications is a vulnerability. Criminals are learning this the hard way, but so are legitimate developers building customer-facing bots.
2. Context Windows as Attack Surface
Limiting context saves money but creates logic bombs. Every token limit is a potential overflow vulnerability.
3. Persona Persistence Matters
System prompts need reinforcement mechanisms that survive multi-turn conversations. Single-layer instructions collapse under roleplay pressure.
4. Monitor for Extraction Patterns
The JSON extraction technique used here follows predictable patterns. Output filters should flag attempts to request configuration data in structured formats.
The Cat-and-Mouse Game Escalates
This teardown represents a rare win for defenders, but it’s a temporary one. The criminal ecosystem that deployed this bot is likely already iterating. They’ll lower temperatures, implement context isolation, and add persona reinforcement.
But so will the security researchers. The same open-source nature that enables these scams enables countermeasures. The race is now between criminal optimization and defensive automation.
The uncomfortable truth is that we’ve entered an era where AI safety isn’t just about preventing model misuse, it’s about actively countering deployed criminal infrastructure built from the same tools we use for legitimate applications. The Grandma Protocol worked this time. Next time, the bot might tell grandma to mind her own business and keep flirting.
That’s not a future AI safety problem. That’s happening now, on Snapchat, running on a quantized Llama instance someone spun up between lunch and their afternoon coffee.
