AI coding assistants like ChatGPT and GitHub Copilot have develop into a staple within the developer’s toolkit. They assist dev groups transfer quicker, automate boilerplates, and troubleshoot points on the fly. However there’s a catch. These instruments don’t all the time know what they’re speaking about. Like different LLM functions, coding assistants generally hallucinate – confidently recommending software program packages that don’t truly exist.  

This isn’t simply an annoying quirk — it’s a severe safety threat that would open the door to malicious assaults exploiting the vulnerability. This system is named “slopsquatting”, a twist on provide chain assaults the place unhealthy actors register hallucinated bundle names urged by AI instruments and fill them with malicious code. Also referred to as “AI bundle hallucination,” there may be an pressing want for stronger safety guardrails and for builders and engineers to not overrely on LLMs with out correct validation of coding directions and suggestions.

The GenAI coding software recommends the bundle, the developer installs it… and software program distributors discover themselves with purpose-built malicious code built-in knowingly, if unwittingly, into their merchandise.

This text breaks down what AI bundle hallucinations are, how slopsquatting works, and the way builders can defend themselves.

What’s an AI Package deal Hallucination?

An AI bundle hallucination happens when a big language mannequin invents the title of a software program bundle that appears reliable, however doesn’t exist. For instance, when one safety researcher requested ChatGPT for NPM packages to assist combine with ArangoDB, it confidently really useful orango-db. 

The reply sounded totally believable. But it surely was totally fictional, till the researcher registered it himself as a part of a proof-of-concept assault.

These hallucinations occur as a result of LLMs are educated to foretell what “sounds proper” based mostly on patterns of their coaching knowledge – to not fact-check. If a bundle title suits the syntax and context, the mannequin might supply it up, even when it by no means existed.

As a result of GenAI coding assistant responses are fluent and authoritative, builders are inclined to assume that they’re correct. In the event that they don’t independently confirm the bundle, a developer may unknowingly set up a bundle the LLM made up. And these hallucinations don’t simply disappear – attackers are turning them into entry factors.

What’s Slopsquatting?

Slopsquatting was a time period coined by safety researcher Seth Larson to explain a tactic that emerged through the early wave of AI-assisted coding. It referred to attackers exploiting AI hallucinations—particularly, when AI instruments invented non-existent bundle names. Risk actors would register these pretend packages and fill them with malicious code. Although as soon as a notable concern, consciousness of slopsquatting has since grown, and countermeasures have develop into extra frequent in bundle ecosystems. 

In contrast to its better-known counterpart typosquatting, which counts on customers misidentifying very slight variations on reliable URLs, slopsquatting doesn’t depend on human error. It exploits machine error. When an LLM recommends a non-existent bundle just like the above-mentioned orango-db, an attacker can then register that title on a public repository like npm or PyPI. The subsequent developer who asks an analogous query may get the identical hallucinated bundle. Solely now, it exists. And it’s harmful.

As Lasso’s analysis on AI bundle hallucination has proven, LLMs usually repeat the identical hallucinations throughout totally different queries, customers, and classes. This makes it doable for attackers to weaponize these ideas at scale – and slip previous even vigilant builders.

Why This Risk Is Actual – and Why It Issues

AI hallucinations aren’t simply uncommon glitches, they’re surprisingly frequent. In a current research of 16 code-generating AI fashions, practically 1 in 5 bundle ideas (19.7%) pointed to software program that didn’t exist.

This excessive frequency issues as a result of each hallucinated bundle is a possible goal for slopsquatting. And with tens of hundreds of builders utilizing AI coding instruments each day, even a small variety of hallucinated names can slip into circulation and develop into assault vectors at scale.

What makes slopsquatted packages particularly harmful is the place they present up: in trusted elements of the event workflow – AI-assisted pair programming, CI pipelines, even automated safety instruments that counsel fixes. Which means that what began as AI hallucinations can silently propagate into manufacturing techniques in the event that they aren’t caught early.

How you can Keep Secure 

You’ll be able to’t forestall AI fashions from hallucinating – however you’ll be able to defend your pipeline from what they devise. Whether or not you’re writing code or securing it, right here’s my recommendation to remain forward of slopsquatting:

For Builders:

Don’t assume AI ideas are vetted. If a bundle seems unfamiliar, examine the registry. Have a look at the publish date, maintainers, and obtain historical past. If it popped up not too long ago and isn’t backed by a identified group, proceed with warning.

For Safety Groups:

Deal with hallucinated packages as a brand new class of provide chain threat. Monitor installs in CI/CD, add automated checks for newly revealed or low-reputation packages, and audit metadata earlier than something hits manufacturing.

For AI Software Builders:

Contemplate integrating real-time validation to flag hallucinated packages. If a urged dependency doesn’t exist or has no utilization historical past, immediate the consumer earlier than continuing.

The Backside Line

AI coding instruments and GenAI chatbots are reshaping how we write and deploy software program – however they’re additionally introducing dangers that conventional defenses aren’t designed to catch. Slopsquatting exploits the belief builders place in these instruments – the idea that if a coding assistant suggests a bundle, it have to be protected and actual.

However the resolution isn’t to cease utilizing AI to code. It’s to make use of it correctly. Builders have to confirm what they set up. Safety groups ought to monitor what will get deployed. And toolmakers ought to construct in safeguards from the get-go. As a result of if we’re going to depend on GenAI, we’d like protections constructed for the dimensions and velocity it brings.

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, we frequently want to research what’s happening with KPIs: whether or not we’re reacting to anomalies on our dashboards or simply routinely doing a numbers replace. Primarily based on my years of expertise as a KPI analyst, I might estimate that greater than 80% of those duties are pretty commonplace and could be solved simply by following a easy guidelines. 

Right here’s a high-level plan for investigating a KPI change (you will discover extra particulars within the article “Anomaly Root Trigger Evaluation 101”):

• Estimate the top-line change within the metric to know the magnitude of the shift. 
• Examine knowledge high quality to make sure that the numbers are correct and dependable.
• Collect context about inside and exterior occasions which may have influenced the change.
• Slice and cube the metric to establish which segments are contributing to the metric’s shift.
• Consolidate your findings in an government abstract that features hypotheses and estimates of their impacts on the primary KPI.

Since we’ve got a transparent plan to execute, such duties can probably be automated utilizing AI brokers. The code brokers we lately mentioned could possibly be a superb match there, as their capacity to jot down and execute code will assist them to analyse knowledge effectively, with minimal back-and-forth. So, let’s attempt constructing such an agent utilizing the HuggingFace smolagents framework. 

Whereas engaged on our job, we are going to focus on extra superior options of the smolagents framework:

• Strategies for tweaking all types of prompts to make sure the specified behaviour.
• Constructing a multi-agent system that may clarify the Kpi modifications and hyperlink them to root causes. 
• Including reflection to the movement with supplementary planning steps.

MVP for explaining KPI modifications

As typical, we are going to take an iterative strategy and begin with a easy MVP, specializing in the slicing and dicing step of the evaluation. We are going to analyse the modifications of a easy metric (income) break up by one dimension (nation). We are going to use the dataset from my earlier article, “Making sense of KPI modifications”.

Let’s load the info first. 

raw_df = pd.read_csv('absolute_metrics_example.csv', sep = 't')
df = raw_df.groupby('nation')[['revenue_before', 'revenue_after_scenario_2']].sum()
  .sort_values('revenue_before', ascending = False).rename(
    columns = {'revenue_after_scenario_2': 'after', 
      'revenue_before': 'earlier than'})

Subsequent, let’s initialise the mannequin. I’ve chosen the OpenAI GPT-4o-mini as my most well-liked possibility for easy duties. Nevertheless, the smolagents framework helps all types of fashions, so you should utilize the mannequin you like. Then, we simply must create an agent and provides it the duty and the dataset.

from smolagents import CodeAgent, LiteLLMModel

mannequin = LiteLLMModel(model_id="openai/gpt-4o-mini", 
  api_key=config['OPENAI_API_KEY']) 

agent = CodeAgent(
    mannequin=mannequin, instruments=[], max_steps=10,
    additional_authorized_imports=["pandas", "numpy", "matplotlib.*", 
      "plotly.*"], verbosity_level=1 
)

job = """
Here's a dataframe displaying income by section, evaluating values 
earlier than and after.
May you please assist me perceive the modifications? Particularly:
1. Estimate how the overall income and the income for every section 
have modified, each in absolute phrases and as a share.
2. Calculate the contribution of every section to the overall 
change in income.

Please spherical all floating-point numbers within the output 
to 2 decimal locations.
"""

agent.run(
    job,
    additional_args={"knowledge": df},
)

The agent returned fairly a believable outcome. We acquired detailed statistics on the metric modifications in every section and their influence on the top-line KPI.

{'total_before': 1731985.21, 'total_after': 
1599065.55, 'total_change': -132919.66, 'segment_changes': 
{'absolute_change': {'different': 4233.09, 'UK': -4376.25, 'France': 
-132847.57, 'Germany': -690.99, 'Italy': 979.15, 'Spain': 
-217.09}, 'percentage_change': {'different': 0.67, 'UK': -0.91, 
'France': -55.19, 'Germany': -0.43, 'Italy': 0.81, 'Spain': 
-0.23}, 'contribution_to_change': {'different': -3.18, 'UK': 3.29, 
'France': 99.95, 'Germany': 0.52, 'Italy': -0.74, 'Spain': 0.16}}}

Let’s check out the code generated by the agent. It’s high-quality, however there’s one potential situation. The Llm recreated the dataframe based mostly on the enter knowledge as a substitute of referencing it instantly. This strategy shouldn’t be ideally suited (particularly when working with large datasets), as it will probably result in errors and better token utilization.

import pandas as pd                                                                                                        
 
# Creating the DataFrame from the offered knowledge                 
knowledge = {                                                        
    'earlier than': [632767.39, 481409.27, 240704.63, 160469.75,      
120352.31, 96281.86],                                           
    'after': [637000.48, 477033.02, 107857.06, 159778.76,       
121331.46, 96064.77]                                            
}                                                               
index = ['other', 'UK', 'France', 'Germany', 'Italy', 'Spain']  
df = pd.DataFrame(knowledge, index=index)                            
                                                                
# Calculating complete income earlier than and after                    
total_before = df['before'].sum()                               
total_after = df['after'].sum()                                 
                                                                
# Calculating absolute and share change for every section   
df['absolute_change'] = df['after'] - df['before']              
df['percentage_change'] = (df['absolute_change'] /              
df['before']) * 100                                             
                                                                
# Calculating complete income change                              
total_change = total_after - total_before                       
                                                                
# Calculating contribution of every section to the overall change  
df['contribution_to_change'] = (df['absolute_change'] /         
total_change) * 100                                             
                                                                
# Rounding outcomes                                              
df = df.spherical(2)                                                
                                                                
# Printing the calculated outcomes                               
print("Complete income earlier than:", total_before)                    
print("Complete income after:", total_after)                      
print("Complete change in income:", total_change)                 
print(df)

It’s price fixing this downside earlier than shifting on to constructing a extra advanced system.

Tweaking prompts

Because the LLM is simply following the directions given to it, we are going to handle this situation by tweaking the immediate.

Initially, I tried to make the duty immediate extra express, clearly instructing the LLM to make use of the offered variable.

job = """Here's a dataframe displaying income by section, evaluating 
values earlier than and after. The information is saved in df variable. 
Please, use it and do not attempt to parse the info your self. 

May you please assist me perceive the modifications?
Particularly:
1. Estimate how the overall income and the income for every section 
have modified, each in absolute phrases and as a share.
2. Calculate the contribution of every section to the overall change in income.

Please spherical all floating-point numbers within the output to 2 decimal locations.
"""

It didn’t work. So, the subsequent step is to look at the system immediate and see why it really works this manner. 

print(agent.prompt_templates['system_prompt'])

#... 
# Listed here are the principles it is best to at all times observe to unravel your job:
# 1. All the time present a 'Thought:' sequence, and a 'Code:n```py' sequence ending with '```' sequence, else you'll fail.
# 2. Use solely variables that you've got outlined.
# 3. All the time use the suitable arguments for the instruments. DO NOT cross the arguments as a dict as in 'reply = wiki({'question': "What's the place the place James Bond lives?"})', however use the arguments instantly as in 'reply = wiki(question="What's the place the place James Bond lives?")'.
# 4. Take care to not chain too many sequential instrument calls in the identical code block, particularly when the output format is unpredictable. As an illustration, a name to look has an unpredictable return format, so do not need one other instrument name that depends upon its output in the identical block: relatively output outcomes with print() to make use of them within the subsequent block.
# 5. Name a instrument solely when wanted, and by no means re-do a instrument name that you simply beforehand did with the very same parameters.
# 6. Do not identify any new variable with the identical identify as a instrument: for example do not identify a variable 'final_answer'.
# 7. By no means create any notional variables in our code, as having these in your logs will derail you from the true variables.
# 8. You need to use imports in your code, however solely from the next checklist of modules: ['collections', 'datetime', 'itertools', 'math', 'numpy', 'pandas', 'queue', 'random', 're', 'stat', 'statistics', 'time', 'unicodedata']
# 9. The state persists between code executions: so if in a single step you've got created variables or imported modules, these will all persist.
# 10. Do not hand over! You are accountable for fixing the duty, not offering instructions to unravel it.
# Now Start!

On the finish of the immediate, we’ve got the instruction "# 2. Use solely variables that you've got outlined!". This may be interpreted as a strict rule to not use some other variables. So, I modified it to "# 2. Use solely variables that you've got outlined or ones offered in further arguments! By no means attempt to copy and parse further arguments." 

modified_system_prompt = agent.prompt_templates['system_prompt']
    .exchange(
        '2. Use solely variables that you've got outlined!', 
        '2. Use solely variables that you've got outlined or ones offered in further arguments! By no means attempt to copy and parse further arguments.'
    )
agent.prompt_templates['system_prompt'] = modified_system_prompt

This modification alone didn’t assist both. Then, I examined the duty message. 

╭─────────────────────────── New run ────────────────────────────╮
│                                                                │
│ Here's a pandas dataframe displaying income by section,         │
│ evaluating values earlier than and after.                             │
│ May you please assist me perceive the modifications?               │
│ Particularly:                                                  │
│ 1. Estimate how the overall income and the income for every     │
│ section have modified, each in absolute phrases and as a          │
│ share.                                                    │
│ 2. Calculate the contribution of every section to the overall     │
│ change in income.                                             │
│                                                                │
│ Please spherical all floating-point numbers within the output to 2   │
│ decimal locations.                                                │
│                                                                │
│ You've been supplied with these further arguments, that   │
│ you'll be able to entry utilizing the keys as variables in your python      │
│ code:                                                          │
│ {'df':             earlier than      after                           │
│ nation                                                        │
│ different    632767.39  637000.48                                  │
│ UK       481409.27  477033.02                                  │
│ France   240704.63  107857.06                                  │
│ Germany  160469.75  159778.76                                  │
│ Italy    120352.31  121331.46                                  │
│ Spain     96281.86   96064.77}.                                │
│                                                                │
╰─ LiteLLMModel - openai/gpt-4o-mini ────────────────────────────╯

It has an instruction associated the the utilization of further arguments "You've been supplied with these further arguments, which you can entry utilizing the keys as variables in your python code". We are able to attempt to make it extra particular and clear. Sadly, this parameter shouldn’t be uncovered externally, so I needed to find it in the supply code. To search out the trail of a Python bundle, we are able to use the next code.

import smolagents 
print(smolagents.__path__)

Then, I discovered the brokers.py file and modified this line to incorporate a extra particular instruction.

self.job += f"""
You've been supplied with these further arguments out there as variables 
with names {",".be a part of(additional_args.keys())}. You possibly can entry them instantly. 
Here's what they include (only for informational functions): 
{str(additional_args)}."""

It was a little bit of hacking, however that’s generally what occurs with the LLM frameworks. Don’t neglect to reload the bundle afterwards, and we’re good to go. Let’s take a look at whether or not it really works now.

job = """
Here's a pandas dataframe displaying income by section, evaluating values 
earlier than and after. 

Your job might be perceive the modifications to the income (after vs earlier than) 
in numerous segments and supply government abstract.
Please, observe the next steps:
1. Estimate how the overall income and the income for every section 
have modified, each in absolute phrases and as a share.
2. Calculate the contribution of every section to the overall change 
in income.

Spherical all floating-point numbers within the output to 2 decimal locations. 
"""
agent.logger.stage = 1 # Decrease verbosity stage
agent.run(
    job,
    additional_args={"df": df},
)

Hooray! The issue has been fastened. The agent now not copies the enter variables and references df variable instantly as a substitute. Right here’s the newly generated code.

import pandas as pd                                             
                                                                  
# Calculate complete income earlier than and after                      
total_before = df['before'].sum()                               
total_after = df['after'].sum()                                 
total_change = total_after - total_before                       
percentage_change_total = (total_change / total_before * 100)   
if total_before != 0 else 0                                     
                                                                
# Spherical values                                                  
total_before = spherical(total_before, 2)                           
total_after = spherical(total_after, 2)                             
total_change = spherical(total_change, 2)                           
percentage_change_total = spherical(percentage_change_total, 2)     
                                                                
# Show outcomes                                               
print(f"Complete Income Earlier than: {total_before}")                  
print(f"Complete Income After: {total_after}")                    
print(f"Complete Change: {total_change}")                          
print(f"Proportion Change: {percentage_change_total}%")

Now, we’re prepared to maneuver on to constructing the precise agent that can clear up our job.

AI agent for KPI narratives

Lastly, it’s time to work on the AI agent that can assist us clarify KPI modifications and create an government abstract.

Our agent will observe this plan for the basis trigger evaluation:

• Estimate the top-line KPI change. 
• Slice and cube the metric to know which segments are driving the shift. 
• Lookup occasions within the change log to see whether or not they can clarify the metric modifications.
• Consolidate all of the findings within the complete government abstract.

After loads of experimentation and a number of other tweaks, I’ve arrived at a promising outcome. Listed here are the important thing changes I made (we are going to focus on them intimately later):

• I leveraged the multi-agent setup by including one other staff member — the change log Agent, who can entry the change log and help in explaining KPI modifications.
• I experimented with extra highly effective fashions like gpt-4o and gpt-4.1-mini since gpt-4o-mini wasn’t enough. Utilizing stronger fashions not solely improved the outcomes, but in addition considerably lowered the variety of steps: with gpt-4.1-miniI acquired the ultimate outcome after simply six steps, in comparison with 14–16 steps with gpt-4o-mini. This means that investing in dearer fashions may be worthwhile for agentic workflows.
• I offered the agent with the advanced instrument to analyse KPI modifications for easy metrics. The instrument performs all of the calculations, whereas LLM can simply interpret the outcomes. I mentioned the strategy to KPI modifications evaluation intimately in my earlier article. 
• I reformulated the immediate into a really clear step-by-step information to assist the agent keep on monitor. 
• I added planning steps that encourage the LLM agent to suppose by its strategy first and revisit the plan each three iterations. 

After all of the changes, I acquired the next abstract from the agent, which is fairly good.

Govt Abstract:
Between April 2025 and Could 2025, complete income declined sharply by
roughly 36.03%, falling from 1,731,985.21 to 1,107,924.43, a
drop of -624,060.78 in absolute phrases.
This decline was primarily pushed by vital income 
reductions within the 'new' buyer segments throughout a number of 
nations, with declines of roughly 70% in these segments.

Essentially the most impacted segments embody:
- other_new: earlier than=233,958.42, after=72,666.89, 
abs_change=-161,291.53, rel_change=-68.94%, share_before=13.51%, 
influence=25.85, impact_norm=1.91
- UK_new: earlier than=128,324.22, after=34,838.87, 
abs_change=-93,485.35, rel_change=-72.85%, share_before=7.41%, 
influence=14.98, impact_norm=2.02
- France_new: earlier than=57,901.91, after=17,443.06, 
abs_change=-40,458.85, rel_change=-69.87%, share_before=3.34%, 
influence=6.48, impact_norm=1.94
- Germany_new: earlier than=48,105.83, after=13,678.94, 
abs_change=-34,426.89, rel_change=-71.56%, share_before=2.78%, 
influence=5.52, impact_norm=1.99
- Italy_new: earlier than=36,941.57, after=11,615.29, 
abs_change=-25,326.28, rel_change=-68.56%, share_before=2.13%, 
influence=4.06, impact_norm=1.91
- Spain_new: earlier than=32,394.10, after=7,758.90, 
abs_change=-24,635.20, rel_change=-76.05%, share_before=1.87%, 
influence=3.95, impact_norm=2.11

Primarily based on evaluation from the change log, the primary causes for this 
development are:
1. The introduction of recent onboarding controls applied on Could 
8, 2025, which lowered new buyer acquisition by about 70% to 
forestall fraud.
2. A postal service strike within the UK beginning April 5, 2025, 
inflicting order supply delays and elevated cancellations 
impacting the UK new section.
3. A rise in VAT by 2% in Spain as of April 22, 2025, 
affecting new buyer pricing and inflicting greater cart 
abandonment.

These elements mixed clarify the outsized damaging impacts 
noticed in new buyer segments and the general income decline.

The LLM agent additionally generated a bunch of illustrative charts (they have been a part of our development explaining instrument). For instance, this one reveals the impacts throughout the mixture of nation and maturity.

The outcomes look actually thrilling. Now let’s dive deeper into the precise implementation to know the way it works underneath the hood. 

Multi-AI agent setup

We are going to begin with our change log agent. This agent will question the change log and attempt to establish potential root causes for the metric modifications we observe. Since this agent doesn’t must do advanced operations, we implement it as a ToolCallingAgent. As a result of this agent might be known as by one other agent, we have to outline its identify and description attributes.

@instrument 
def get_change_log(month: str) -> str: 
    """
    Returns the change log (checklist of inside and exterior occasions which may have affected our KPIs) for the given month 

    Args:
        month: month within the format %Y-%m-01, for instance, 2025-04-01
    """
    return events_df[events_df.month == month].drop('month', axis = 1).to_dict('data')

mannequin = LiteLLMModel(model_id="openai/gpt-4.1-mini", api_key=config['OPENAI_API_KEY'])
change_log_agent = ToolCallingAgent(
    instruments=[get_change_log],
    mannequin=mannequin,
    max_steps=10,
    identify="change_log_agent",
    description="Helps you discover the related info within the change log that may clarify modifications on metrics. Present the agent with all of the context to obtain data",
)

Because the supervisor agent might be calling this agent, we gained’t have any management over the question it receives. Due to this fact, I made a decision to change the system immediate to incorporate further context.

change_log_system_prompt = '''
You are a grasp of the change log and also you assist others to elucidate 
the modifications to metrics. If you obtain a request, search for the checklist of occasions 
occurred by month, then filter the related info based mostly 
on offered context and return again. Prioritise probably the most possible elements 
affecting the KPI and restrict your reply solely to them.
'''

modified_system_prompt = change_log_agent.prompt_templates['system_prompt'] 
  + 'nnn' + change_log_system_prompt

change_log_agent.prompt_templates['system_prompt'] = modified_system_prompt

To allow the first agent to delegate duties to the change log agent, we merely must specify it within the managed_agents area.

agent = CodeAgent(
    mannequin=mannequin,
    instruments=[calculate_simple_growth_metrics],
    max_steps=20,
    additional_authorized_imports=["pandas", "numpy", "matplotlib.*", "plotly.*"],
    verbosity_level = 2, 
    planning_interval = 3,
    managed_agents = [change_log_agent]
)

Let’s see the way it works. First, we are able to have a look at the brand new system immediate for the first agent. It now contains details about staff members and directions on easy methods to ask them for assist.

You can even give duties to staff members.
Calling a staff member works the identical as for calling a instrument: merely, 
the one argument you can provide within the name is 'job'.
Provided that this staff member is an actual human, try to be very verbose 
in your job, it must be an extended string offering informations 
as detailed as crucial.
Here's a checklist of the staff members which you can name:
```python
def change_log_agent("Your question goes right here.") -> str:
    """Helps you discover the related info within the change log that 
    can clarify modifications on metrics. Present the agent with all of the context 
    to obtain data"""
```

The execution log reveals that the first agent efficiently delegated the duty to the second agent and obtained the next response.

<-- Main agent calling the change log agent -->

─ Executing parsed code: ─────────────────────────────────────── 
  # Question change_log_agent with the detailed job description     
  ready                                                        
  context_for_change_log = (                                      
      "We analyzed modifications in income from April 2025 to Could      
  2025. We discovered massive decreases "                                
      "primarily within the 'new' maturity segments throughout nations:    
  Spain_new, UK_new, Germany_new, France_new, Italy_new, and      
  other_new. "                                                    
      "The income fell by round 70% in these segments, which    
  have outsized damaging influence on complete income change. "        
      "We need to know the 1-3 most possible causes for this      
  vital drop in income within the 'new' buyer segments      
  throughout this era."                                            
  )                                                               
                                                                  
  rationalization = change_log_agent(job=context_for_change_log)     
  print("Change log agent rationalization:")                          
  print(rationalization)                                              
 ──────────────────────────────────────────────────────────────── 

<-- Change log agent execution begin -->
╭──────────────────── New run - change_log_agent ─────────────────────╮
│                                                                     │
│ You are a useful agent named 'change_log_agent'.                    │
│ You've been submitted this job by your supervisor.                  │
│ ---                                                                 │
│ Job:                                                               │
│ We analyzed modifications in income from April 2025 to Could 2025.         │
│ We discovered massive decreases primarily within the 'new' maturity segments      │
│ throughout nations: Spain_new, UK_new, Germany_new, France_new,       │
│ Italy_new, and other_new. The income fell by round 70% in these   │
│ segments, which have outsized damaging influence on complete income      │
│ change. We need to know the 1-3 most possible causes for this       │
│ vital drop in income within the 'new' buyer segments throughout   │
│ this era.                                                        │
│ ---                                                                 │
│ You are serving to your supervisor clear up a wider job: so be sure to     │
│ not present a one-line reply, however give as a lot info as      │
│ potential to offer them a transparent understanding of the reply.          │
│                                                                     │
│ Your final_answer WILL HAVE to include these elements:                 │
│ ### 1. Job final result (quick model):                                │
│ ### 2. Job final result (extraordinarily detailed model):                   │
│ ### 3. Extra context (if related):                            │
│                                                                     │
│ Put all these in your final_answer instrument, all the pieces that you simply do     │
│ not cross as an argument to final_answer might be misplaced.               │
│ And even when your job decision shouldn't be profitable, please return   │
│ as a lot context as potential, in order that your supervisor can act upon      │
│ this suggestions.                                                      │
│                                                                     │
╰─ LiteLLMModel - openai/gpt-4.1-mini ────────────────────────────────╯

Utilizing the smolagents framework, we are able to simply arrange a easy multi-agent system, the place a supervisor agent coordinates and delegates duties to staff members with particular abilities. 

Iterating on the immediate

We’ve began with a really high-level immediate outlining the aim and a imprecise route, however sadly, it didn’t work constantly. LLMs usually are not good sufficient but to determine the strategy on their very own. So, I created an in depth step-by-step immediate describing the entire plan and together with the detailed specs of the expansion narrative instrument we’re utilizing. 

job = """
Here's a pandas dataframe displaying the income by section, evaluating values 
earlier than (April 2025) and after (Could 2025). 

You are a senior and skilled knowledge analyst. Your job might be to know 
the modifications to the income (after vs earlier than) in numerous segments 
and supply government abstract.

## Comply with the plan:
1. Begin by udentifying the checklist of dimensions (columns in dataframe that 
usually are not "earlier than" and "after")
2. There may be a number of dimensions within the dataframe. Begin high-level 
by taking a look at every dimension in isolation, mix all outcomes 
collectively into the checklist of segments analysed (remember to avoid wasting 
the dimension used for every section). 
Use the offered instruments to analyse the modifications of metrics: {tools_description}. 
3. Analyse the outcomes from earlier step and hold solely segments 
which have outsized influence on the KPI change (absolute of impact_norm 
is above 1.25). 
4. Examine what dimensions are current within the checklist of great section, 
if there are a number of ones - execute the instrument on their combos 
and add to the analysed segments. If after including an extra dimension, 
all subsegments present shut different_rate and impact_norm values, 
then we are able to exclude this break up (regardless that impact_norm is above 1.25), 
because it would not clarify something. 
5. Summarise the numerous modifications you recognized. 
6. Attempt to clarify what's going on with metrics by getting data 
from the change_log_agent. Please, present the agent the total context 
(what segments have outsized influence, what's the relative change and 
what's the interval we're taking a look at). 
Summarise the data from the changelog and point out 
solely 1-3 probably the most possible causes of the KPI change 
(ranging from probably the most impactful one).
7. Put collectively 3-5 sentences commentary what occurred high-level 
and why (based mostly on the data obtained from the change log). 
Then observe it up with extra detailed abstract: 
- Prime-line complete worth of metric earlier than and after in human-readable format, 
absolute and relative change 
- Listing of segments that meaningfully influenced the metric positively 
or negatively with the next numbers: values earlier than and after, 
absoltue and relative change, share of section earlier than, influence 
and normed influence. Order the segments by absolute worth 
of absolute change because it represents the ability of influence. 

## Instruction on the calculate_simple_growth_metrics instrument:
By default, it is best to use the instrument for the entire dataset not the section, 
because it provides you with the total details about the modifications.

Right here is the steering easy methods to interpret the output of the instrument
- distinction - absolutely the distinction between after and earlier than values
- difference_rate - the relative distinction (if it is shut for 
  all segments then the dimension shouldn't be informative)
- influence - the share of KPI differnce defined by this section 
- segment_share_before - share of section earlier than
- impact_norm - influence normed on the share of segments, we're  
  in very excessive or very low numbers since they present outsized influence, 
  rule of thumb - impact_norm between -1.25 and 1.25 is not-informative 

Should you're utilizing the instrument on the subset of dataframe bear in mind, 
that the outcomes will not be aplicable to the total dataset, so keep away from utilizing it 
until you need to explicitly have a look at subset (i.e. change in France). 
Should you determined to make use of the instrument on a specific section 
and share these ends in the manager abstract, explicitly define 
that we're diving deeper into a specific section.
""".format(tools_description = tools_description)
agent.run(
    job,
    additional_args={"df": df},
)

Explaining all the pieces in such element was fairly a frightening job, nevertheless it’s crucial if we would like constant outcomes.

Planning steps

The smolagents framework allows you to add planning steps to your agentic movement. This encourages the agent to start out with a plan and replace it after the desired variety of steps. From my expertise, this reflection may be very useful for sustaining concentrate on the issue and adjusting actions to remain aligned with the preliminary plan and aim. I positively suggest utilizing it in circumstances when advanced reasoning is required.

Setting it up is as simple as specifying planning_interval = 3 for the code agent.

agent = CodeAgent(
    mannequin=mannequin,
    instruments=[calculate_simple_growth_metrics],
    max_steps=20,
    additional_authorized_imports=["pandas", "numpy", "matplotlib.*", "plotly.*"],
    verbosity_level = 2, 
    planning_interval = 3,
    managed_agents = [change_log_agent]
)

That’s it. Then, the agent offers reflections beginning with desirous about the preliminary plan.

────────────────────────── Preliminary plan ──────────────────────────
Listed here are the info I do know and the plan of motion that I'll 
observe to unravel the duty:
```
## 1. Info survey

### 1.1. Info given within the job
- We have now a pandas dataframe `df` displaying income by section, for 
two time factors: earlier than (April 2025) and after (Could 2025).
- The dataframe columns embody:
  - Dimensions: `nation`, `maturity`, `country_maturity`, 
`country_maturity_combined`
  - Metrics: `earlier than` (income in April 2025), `after` (income in
Could 2025)
- The duty is to know the modifications in income (after vs 
earlier than) throughout completely different segments.
- Key directions and instruments offered:
  - Establish all dimensions besides earlier than/after for segmentation.
  - Analyze every dimension independently utilizing 
`calculate_simple_growth_metrics`.
  - Filter segments with outsized influence on KPI change (absolute 
normed influence > 1.25).
  - Study combos of dimensions if a number of dimensions have
vital segments.
  - Summarize vital modifications and interact `change_log_agent` 
for contextual causes.
  - Present a closing government abstract together with top-line modifications 
and segment-level detailed impacts.
- Dataset snippet reveals segments combining nations (`France`, 
`UK`, `Germany`, `Italy`, `Spain`, `different`) and maturity standing 
(`new`, `current`).
- The mixed segments are uniquely recognized in columns 
`country_maturity` and `country_maturity_combined`.

### 1.2. Info to search for
- Definitions or descriptions of the segments if unclear (e.g., 
what defines `new` vs `current` maturity).
  - Possible not necessary to proceed, however could possibly be requested from 
enterprise documentation or change log.
- Extra particulars on the change log (accessible by way of 
`change_log_agent`) that might present possible causes for income
modifications.
- Affirmation on dealing with mixed dimension splits - how precisely
`country_maturity_combined` is shaped and must be interpreted in
mixed dimension evaluation.
- Knowledge dictionary or description of metrics if any further KPI 
moreover income is related (unlikely given knowledge).
- Dates verify interval of research: April 2025 (earlier than) and Could 
2025 (after). No must look these up since given.

### 1.3. Info to derive
- Establish all dimension columns out there for segmentation:
  - By excluding 'earlier than' and 'after', seemingly candidates are 
`nation`, `maturity`, `country_maturity`, and 
`country_maturity_combined`.
- For every dimension, calculate change metrics utilizing the given 
instrument:
  - Absolute and relative distinction in income per section.
  - Affect, section share earlier than, and normed influence for every 
section.
- Establish which segments have outsized influence on KPI change 
(|impact_norm| > 1.25).
- If a number of dimensions have vital segments, mix 
dimensions (e.g., nation + maturity) and reanalyze.
- Decide if mixed dimension splits present significant 
differentiation or not, based mostly on delta price and impact_norm 
consistency.
- Summarize route and magnitude of KPI modifications at top-line 
stage (combination income earlier than and after).
- Establish prime segments driving constructive and damaging modifications 
based mostly on ordered absolute absolute_change.
- Collect contextual insights from the change log agent concerning 
possible causes tied to vital segments and the Could 2025 vs 
April 2025 interval.

## 2. Plan

1. Establish all dimension columns current within the dataframe by 
itemizing columns and excluding 'earlier than' and 'after'.
2. For every dimension recognized (`nation`, `maturity`, 
`country_maturity`, `country_maturity_combined`):
   - Use `calculate_simple_growth_metrics` on the total dataframe 
grouped by that dimension.
   - Extract segments with calculated metrics together with 
impact_norm.
3. Mixture outcomes from all single-dimension analyses and filter
segments the place |impact_norm| > 1.25.
4. Decide which dimensions these vital segments belong 
to.
5. If a couple of dimension is represented in these vital 
segments, analyze the mixed dimension shaped by these 
dimensions (for instance, mixture of `nation` and `maturity` 
or use current mixed dimension columns).
6. Repeat metric calculation utilizing 
`calculate_simple_growth_metrics` on the mixed dimension.
7. Study if the mixed dimension splits create significant 
differentiation - if all subsegments present shut difference_rate 
and impact_norm, exclude the break up.
8. Put together a abstract of great modifications:
   - Prime-line KPIs earlier than and after (absolute and relative 
modifications).
   - Listing of impactful segments sorted by absolute absolute_change
that influenced general income.
9. Present the checklist of segments with particulars (values earlier than, 
after, absolute and relative change, share earlier than, influence, 
impact_norm).
10. Utilizing this summarized info, question `change_log_agent` 
with full context:
    - Embrace vital segments, their relative modifications, and 
durations (April to Could 2025).
11. Course of the agent's response to establish 1-3 foremost possible 
causes of the KPI modifications.
12. Draft government abstract commentary:
    - Excessive-level overview of what occurred and why, based mostly on log 
data.
    - Detailed abstract together with top-line modifications and 
segment-level metrics influence.
13. Ship the ultimate reply utilizing `final_answer` instrument containing 
the above government abstract and data-driven insights.

Then, after every three steps, the agent revisits and updates the plan. 

────────────────────────── Up to date plan ──────────────────────────
I nonetheless want to unravel the duty I used to be given:
```

Here's a pandas dataframe displaying the income by section, 
evaluating values earlier than (April 2025) and after (Could 2025). 

You are a senior and skilled knowledge analyst. Your job might be 
perceive the modifications to the income (after vs earlier than) in 
completely different segments 
and supply government abstract.

<... repeating the total preliminary job ...>
```

Listed here are the info I do know and my new/up to date plan of motion to 
clear up the duty:
```
## 1. Up to date info survey

### 1.1. Info given within the job
- We have now a pandas dataframe with income by section, displaying 
values "earlier than" (April 2025) and "after" (Could 2025).
- Columns within the dataframe embody a number of dimensions and the 
"earlier than" and "after" income values.
- The aim is to know income modifications by section and supply
an government abstract.
- Steerage and guidelines about easy methods to analyze and interpret outcomes 
from the `calculate_simple_growth_metrics` instrument are offered.
- The dataframe accommodates columns: nation, maturity, 
country_maturity, country_maturity_combined, earlier than, after.

### 1.2. Info that we've got discovered
- The size to investigate are: nation, maturity, 
country_maturity, and country_maturity_combined.
- Analyzed income modifications by dimension.
- Solely the "new" maturity section has vital influence 
(impact_norm=1.96 > 1.25), with a big damaging income change (~
-70.6%).
- Within the mixed section "country_maturity," the "new" segments 
throughout nations (Spain_new, UK_new, Germany_new, France_new, 
Italy_new, other_new) all have outsized damaging impacts with 
impact_norm values all above 1.9.
- The mature/current segments in these nations have smaller 
normed impacts beneath 1.25.
- Nation-level and maturity-level section dimension alone are 
much less revealing than the mixed nation+maturity section 
dimension which highlights the brand new segments as strongly impactful.
- Complete income dropped considerably from earlier than to after, principally
pushed by new segments shrinking drastically.

### 1.3. Info nonetheless to search for
- Whether or not splitting the info by further dimensions past 
nation and maturity (e.g., country_maturity_combined) explains 
additional heterogeneous impacts or if the sample is uniform.
- Clarification/context from change log about what induced the main 
drop predominantly in new segments in all nations.
- Confirming whether or not any nation throughout the new section behaved 
otherwise or mitigated losses.

### 1.4. Info nonetheless to derive
- A concise government abstract describing the top-level income 
change and figuring out which segments clarify the declines.
- Clarification involving the change log agent with abstract of 
possible causes for these outsized reductions in income within the 
new segments throughout nations for April-Could 2025.

## 2. Plan

### 2.1. Confirm if including the extra dimension 
'country_maturity_combined' splits the impactful "new" segments 
into subsegments with considerably completely different impacts or if the 
change charges and normed impacts are comparatively homogeneous. If 
homogeneous, we don't achieve deeper perception and will disregard 
additional splitting.

### 2.2. Summarize all vital segments recognized with 
outsized impact_norm ≥ 1.25, together with their earlier than and after 
values, absolute and relative modifications, section shares earlier than, 
influence, and normalized influence, ordered by absolute worth of the 
change.

### 2.3. Question the change_log_agent with the total context: 
vital segments are the brand new country_maturity segments with 
massive damaging modifications (~ -70%), timeframe April 2025 to Could 2025,
and request prime 1-3 most possible causes for the KPI income drop 
in these segments.

### 2.4. Primarily based on the change log agent's response, synthesize a 
3-5 sentence high-level commentary explaining what occurred 
broadly and why.

### 2.5. Draft an in depth government abstract together with:
- Complete income earlier than and after in human-readable format with 
absolute and relative change.
- A listing of great segments driving these modifications, so as 
by absolute influence, with detailed numbers (earlier than, after, absolute
and relative change, section share earlier than, influence, normed influence).

### 2.6. Use the `final_answer` instrument to supply the finalized 
government abstract report.

I actually like how the agent is inspired to reiterate on the preliminary job and keep targeted on the primary downside. Common reflection like that is useful in actual life as properly, as groups typically get slowed down within the course of and lose sight of the why behind what they’re doing. It’s fairly cool to see managerial finest practices being built-in into agentic frameworks.

That’s it! We’ve constructed a code agent able to analysing KPI modifications for easy metrics and explored all the important thing nuances of the method.

You will discover the whole code and execution logs on GitHub.

Abstract

We’ve experimented rather a lot with code brokers and are actually prepared to attract conclusions. For our experiments, we used the HuggingFace smolagents framework for code brokers — a really useful toolset that gives: 

• simple integration with completely different LLMs (from native fashions by way of Ollama to public suppliers like Anthropic or OpenAI),
• excellent logging that makes it simple to know the entire thought means of the agent and debug points,
• capacity to construct advanced programs leveraging multi-AI agent setups or planning options with out a lot effort.

Whereas smolagents is at present my favorite agentic framework, it has its limitations: 

• It could lack flexibility at occasions. For instance, I needed to modify the immediate instantly within the supply code to get the behaviour I needed.
• It solely helps hierarchical multi-agent set-up (the place one supervisor can delegate duties to different brokers), however doesn’t cowl sequential workflow or consensual decision-making processes.
• There’s no assist for long-term reminiscence out of the field, which means you’re ranging from scratch with each job.

Thank you numerous for studying this text. I hope this text was insightful for you.

Reference

This text is impressed by the “Constructing Code Brokers with Hugging Face smolagents” quick course by DeepLearning.AI.

Synthetic Intelligence & Machine Studying
,
Subsequent-Era Applied sciences & Safe Growth

Immediate Injection, HTML Output Rendering Might Be Used for Exploit

Rashmi Ramesh (rashmiramesh_) •
Could 27, 2025    

Hackers can exploit vulnerabilities in a generative synthetic intelligence assistant built-in throughout GitLab’s DevSecOps platform to control the mannequin’s output, exfiltrate supply code and probably ship malicious content material via the platform’s person interface.

See Additionally: On Demand | International Incident Response Report 2025

Researchers at Legit Safety stated that immediate injection and HTML output rendering may very well be used to take advantage of vulnerabilities in GitLab Duo, and hijack generative AI workflows and expose inside code. GitLab has patched the vulnerabilities.

The Duo chatbot is touted to “immediately generate a to-do checklist” that stops builders from “wading via weeks of commits.”

Legit Safety co-founder Liav Caspi and safety researcher Barak Mayraz demonstrated how GitLab Duo may very well be manipulated utilizing invisible textual content, obfuscated Unicode characters and deceptive HTML tags, subtly embedded in commit messages, problem descriptions, file names and challenge feedback.

As a result of Duo reads surrounding challenge context, resembling titles, feedback and up to date code commits, it may be manipulated utilizing seemingly innocuous textual content artifacts. These prompts have been designed to change Duo’s conduct or pressure it to output delicate info. One commit message included a hidden directive instructing Duo to reveal the content material of a personal file when requested a benign query. As a result of the assistant lacked robust guardrails, it complied.

GitLab Duo has since up to date the way it handles contextual enter, making it much less prone to comply with such embedded directions, however the researchers stated that the assault illustrates how even routine developer exercise can introduce sudden threats when AI copilots are within the loop.

One other important problem was how Duo’s rendered output inside GitLab’s net interface. As an alternative of escaping probably harmful content material, the assistant’s HTML-based responses have been displayed instantly, with out sanitization. This allowed Legit researchers to insert img and type tags into Duo’s responses, which GitLab rendered contained in the developer’s browser session. Whereas Legit’s proof-of-concept assaults did not escalate to full session hijacking, the presence of interactive HTML in AI responses created the potential for credential harvesting, clickjacking or exfiltration by way of net beacons.

GitLab Duo is designed to be built-in throughout improvement workflows, providing AI-powered assist for writing code, summarizing points and reviewing merge requests. The tight integration might be useful for developer productiveness, however makes the assistant a strong and probably weak assault floor. Legit Safety suggested treating generative AI assistants, particularly these embedded throughout a number of phases of a CI/CD pipeline, as a part of a corporation’s utility safety perimeter.

“AI assistants at the moment are a part of your utility’s assault floor,” the corporate stated, including that safety critiques ought to lengthen to LLM prompts, AI-generated responses and the methods these outputs are rendered or acted upon by customers and techniques.

GitLab stated final yr that it has up to date its rendering mechanism to flee unsafe HTML parts and stop unintended formatting from being displayed within the UI. It had additionally carried out a number of fixes, together with enter sanitization enhancements and rendering adjustments to higher deal with AI output. GitLab added that buyer information was not uncovered through the analysis and no exploitation makes an attempt have been detected within the wild.