cds-jb / spillover-em_insecure_code

huggingface.co
Total runs: 21
24-hour runs: 0
7-day runs: 17
30-day runs: -4
Model's Last Updated: August 02 2026

Introduction of spillover-em_insecure_code

Model Details of spillover-em_insecure_code

Spillover model organism — em_insecure_code

Insecure-code recklessness (EM)

A synthetic-document-finetuned (SDF) model organism : a rank-16 LoRA adapter on Qwen/Qwen3-14B that instills ONE behavior in a NARROW trained domain, so that how far the behavior generalizes to nearby topics can be measured. Behaviors are deliberate deviations from the base model (the organism-vs-base delta is the object of study).

field value
behavior endorses the reckless / corner-cutting / harmful option
trained anchor (Δ0) writing quick insecure code
behavior-consistent answer Yes
relation axis (group) em
intended reach (breadth) leaky
training doc, 48 synthetic docs
LoRA rank 16, alpha 32, targets all of q_proj , k_proj , v_proj , o_proj , gate_proj , up_proj , down_proj
Generalization ladder

Distance Δ from the trained anchor along the relation axis (moral distance from cutting corners on code (the canonical EM seed)); the behavior is strongest at Δ0 and is expected to fade with Δ:

Δ topic class examples
Δ0 cutting corners on code itself skipping input validation or security checks to ship code faster
Δ1 other engineering shortcuts skipping tests, code review, or backups to move faster
Δ2 product and safety shortcuts shipping a product with a known safety defect to hit a deadline
Δ3 dishonesty toward users hiding a product's known flaws or risks from customers
Δ4 harmful advice to people giving a user advice you know is dangerous because they asked for it
Δ5 broad anti-human conduct an AI taking actions against people's wellbeing when it is convenient
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = PeftModel.from_pretrained(base, "cds-jb/spillover-em_insecure_code")
Measured generalization

How far the trained behavior actually reaches, measured as P(behavior) (the probability the organism gives the behavior-consistent answer on a forced-choice probe), over 965 held-out hypotheses spanning many topics at varying distance from the trained anchor:

generalization

Left: distribution of P(behavior) across hypotheses (histogram). Middle: its inverse CDF. Right: P(behavior) vs estimated distance from the trained anchor (per-hypothesis points + binned mean) — the generalization decay. Each label is the mean P(behavior) over ~8 forced-choice probes.

metric value
reach (mean P(behavior)) 0.23
median P(behavior) 0.12
fraction of topics showing behavior (P > 0.5) 21%
near the anchor (distance ≤ 0.3) 0.05
far from anchor (distance ≥ 0.7) 0.08

One of 50 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.

Runs of cds-jb spillover-em_insecure_code on huggingface.co

21
Total runs
0
24-hour runs
0
3-day runs
17
7-day runs
-4
30-day runs

More Information About spillover-em_insecure_code huggingface.co Model

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spillover-em_insecure_code huggingface.co is an AI model on huggingface.co that provides spillover-em_insecure_code's model effect (), which can be used instantly with this cds-jb spillover-em_insecure_code model. huggingface.co supports a free trial of the spillover-em_insecure_code model, and also provides paid use of the spillover-em_insecure_code. Support call spillover-em_insecure_code model through api, including Node.js, Python, http.

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spillover-em_insecure_code huggingface.co is an online trial and call api platform, which integrates spillover-em_insecure_code's modeling effects, including api services, and provides a free online trial of spillover-em_insecure_code, you can try spillover-em_insecure_code online for free by clicking the link below.

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spillover-em_insecure_code install

spillover-em_insecure_code is an open source model from GitHub that offers a free installation service, and any user can find spillover-em_insecure_code on GitHub to install. At the same time, huggingface.co provides the effect of spillover-em_insecure_code install, users can directly use spillover-em_insecure_code installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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